This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
1

A Survey on Transfer Learning
Sinno Jialin Pan and Qiang Yang Fellow, IEEE
Abstract—A major assumption in many machine learning and data mining algorithms is that the training and future data must bein the same feature space and have the same distribution. However, in many real-world applications, this assumption may not hold.
For example, we sometimes have a classification task in one domain of interest, but we only have sufficient training data in anotherdomain of interest, where the latter data may be in a different feature space or follow a different data distribution. In such cases,
knowledge transfer, if done successfully, would greatly improve the performance of learning by avoiding much expensive data labeling
efforts. In recent years, transfer learning has emerged as a new learning framework to address this problem. This survey focuses on
categorizing and reviewing the current progress on transfer learning for classification, regression and clustering problems. In this survey,
we discuss the relationship between transfer learning and other related machine learning techniques such as domain adaptation, multitask learning and sample selection bias, as well as co-variate shift. We also explore some potential future issues in transfer learning
research.
Index Terms—Transfer Learning, Survey, Machine Learning, Data Mining.

✦

1

I NTRODUCTION

Data mining and machine learning technologies have alreadyachieved significant success in many knowledge engineering
areas including classification, regression and clustering (e.g.,[1], [2]). However, many machine learning methods work wellonly under a common assumption: the training and test data are
drawn from the same feature space and the same distribution.
When the distribution changes, most statistical models need tobe rebuilt from scratch using newly collected training data. In
many real world applications, it is expensive or impossible tore-collect the needed training data and rebuild the models. It
would be nice to reduce the need and effort to re-collect thetraining data. In such cases, knowledge transfer or transfer
learning between task domains would be desirable.
Many examples in knowledge engineering can be foundwhere transfer learning can truly be beneficial. One example
is Web document classification [3], [4], [5], where our goal
is to classify a given Web document into several predefined
categories. As an example in the area of Web-documentclassification (see, e.g., [6]), the labeled examples may be
the university Web pages that are associated with category
information obtained through previous manual-labeling efforts.
For a classification task on a newly created Web site where thedata features or data distributions may be different, there may
be a lack of labeled training data. As a result, we may not beable to directly apply the Web-page classifiers learned on theuniversity Web site to the new Web site. In such cases, it would
be helpful if we could transfer the classification knowledge
into the new domain.
The need for transfer learning may arise when the data canbe easily outdated. In this case, the labeled data obtained in
one time period may not follow the same distribution in alater time period. For example, in indoor WiFi localization
Department of Computer Science and Engineering, Hong Kong University of
Science and Technology, Clearwater Bay, Kowloon, Hong Kong
Emails: {sinnopan, qyang}@cse.ust.hk

Digital Object Indentifier 10.1109/TKDE.2009.191

problems, which aims to detect a user’s current location basedon previously collected WiFi data, it is very expensive tocalibrate WiFi data for building localization models in a largescale environment, because a user needs to label a large
collection of WiFi signal data at each location. However, theWiFi signal-strength values may be a function of time, device
or other dynamic factors. A model trained in one time periodor on one device may cause the performance for location
estimation in another time period or on another device to be
reduced. To reduce the re-calibration effort, we might wish toadapt the localization model trained in one time period (thesource domain) for a new time period (the target domain), or
to adapt the localization model trained on a mobile device (thesource domain) for a new mobile device (the target domain),as done in [7].
As a third example, consider the problem of sentimentclassification, where our task is to automatically classify thereviews on a product, such as a brand of camera, into positive
and negative views. For this classification task, we need to
first collect many reviews of the product and annotate them.
We would then train a classifier on the reviews with theircorresponding labels. Since the distribution of review data
among different types of products can be very different, tomaintain good classification performance, we need to collecta large amount of labeled data in order to train the reviewclassification models for each product. However, this datalabeling process can be very expensive to do. To reduce theeffort for annotating reviews for various products, we may
want to adapt a classification model that is trained on someproducts to help learn classification models for some otherproducts. In such cases, transfer learning can save a significant
amount of labeling effort [8].
In this survey article, we give a comprehensive overview oftransfer learning for classification, regression and clusteringdeveloped in machine learning and data mining areas. There
has been a large amount of work on transfer learning forreinforcement learning in the machine learning literature (e.g.,1041-4347/$25.00 © 2009 IEEE

This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
2[9], [10]). However, in this paper, we only focus on transfer
learning for classification, regression and clustering problems
that are related more closely to data mining tasks. By doing
the survey, we hope to provide a useful resource for the data
mining and machine learning community.
The rest of the survey is organized as follows. In the next
four sections, we first give a general overview and definesome notations we will use later. We then briefly survey thehistory of transfer learning, give a unified definition of transfer
learning and categorize transfer learning into three different
settings (given in Table 2 and Figure 2). For each setting, we
review different approaches, given in Table 3 in detail. After
that, in Section 6, we review some current research on the
topic of “negative transfer”, which happens when knowledgetransfer has a negative impact on target learning. In Section 7,
we introduce some successful applications of transfer learningand list some published data sets and software toolkits fortransfer learning research. Finally, we conclude the article witha discussion of future works in Section 8.

2

OVERVIEW

2.1 A Brief History of Transfer Learning
Traditional data mining and machine learning algorithms makepredictions on the future data using statistical models that aretrained on previously collected labeled or unlabeled training
data [11], [12], [13]. Semi-supervised classification [14], [15],[16], [17] addresses the problem that the labeled data may
be too few to build a good classifier, by making use of alarge amount of unlabeled data and a small amount of labeled
data. Variations of supervised and semi-supervised learningfor imperfect datasets have been studied; for example, Zhu
and Wu [18] have studied how to deal with the noisy classlabel problems. Yang et al. considered cost-sensitive learning[19] when additional tests can be made to future samples.
Nevertheless, most of them assume that the distributions of
the labeled and unlabeled data are the same. Transfer learning,in contrast, allows the domains, tasks, and distributions usedin training and testing to be different. In the real world, we
observe many examples of transfer learning. For example,
we may find that learning to recognize apples might help torecognize pears. Similarly, learning to play the electronic organ
may help facilitate learning the piano. The study of Transfer
learning is motivated by the fact that people can intelligently
apply knowledge learned previously to solve new problems
faster or with better solutions. The fundamental motivation
for Transfer learning in the field of machine learning was
discussed in a NIPS-95 workshop on “Learning to Learn”
1, which focused on the need for lifelong machine-learning
methods that retain and reuse previously learned knowledge.
Research on transfer learning has attracted more andmore attention since 1995 in different names: learning tolearn, life-long learning, knowledge transfer, inductive transfer, multi-task learning, knowledge consolidation, contextsensitive learning, knowledge-based inductive bias, meta learning, and incremental/cumulative learning [20]. Among these,
1. http://socrates.acadiau.ca/courses/comp/dsilver/NIPS95 LTL/
transfer.workshop.1995.html

a closely related learning technique to transfer learning is
the multi-task learning framework [21], which tries to learn
multiple tasks simultaneously even when they are different.
A typical approach for multi-task learning is to uncover thecommon (latent) features that can benefit each individual task.
In 2005, the Broad Agency Announcement (BAA) 05-29
of Defense Advanced Research Projects Agency (DARPA)’s
Information Processing Technology Office (IPTO) 2 gave a
new mission of transfer learning: the ability of a system torecognize and apply knowledge and skills learned in previous
tasks to novel tasks. In this definition, transfer learning aims
to extract the knowledge from one or more source tasks andapplies the knowledge to a target task. In contrast to multi-task
learning, rather than learning all of the source and target tasks
simultaneously, transfer learning cares most about the target
task. The roles of the source and target tasks are no longer
symmetric in transfer learning.
Figure 1 shows the difference between the learning
processes of traditional and transfer learning techniques. As
we can see, traditional machine learning techniques try to learn
each task from scratch, while transfer learning techniques try
to transfer the knowledge from some previous tasks to a target
task when the latter has fewer high-quality training data.

(a) Traditional Machine Learning(b) Transfer Learning

Fig. 1. Different Learning Processes between Traditional
Machine Learning and Transfer Learning
Today, transfer learning methods appear in several top
venues, most notably in data mining (ACM KDD, IEEE ICDM
and PKDD, for example), machine learning (ICML, NIPS,
ECML, AAAI and IJCAI, for example) and applications ofmachine learning and data mining (ACM SIGIR, WWW andACL for example) 3 . Before we give different categorizationsof transfer learning, we first describe the notations used in thisarticle.
2.2 Notations and Definitions
In this section, we introduce some notations and definitions
that are used in this survey. First of all, we give the definitionsof a “domain” and a “task”, respectively.
In this survey, a domain D consists of two components: afeature space X and a marginal probability distribution P (X),
where X = {x1 , . . . , xn } ∈ X . For example, if our learning
2. http://www.darpa.mil/ipto/programs/tl/tl.asp
3. We summarize a list of conferences and workshops where transfer
learning papers appear in these few years in the following webpage forreference, http://www.cse.ust.hk/∼sinnopan/conferenceTL.htm

This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
3

task is document classification, and each term is taken as abinary feature, then X is the space of all term vectors, xi
is the ith term vector corresponding to some documents, and
X is a particular learning sample. In general, if two domains
are different, then they may have different feature spaces ordifferent marginal probability distributions.
Given a specific domain, D = {X , P (X)}, a task consistsof two components: a label space Y and an objective predictivefunction f (·) (denoted by T = {Y, f (·)}), which is notobserved but can be learned from the training data, which
consist of pairs {xi , yi }, where xi ∈ X and yi ∈ Y. The
function f (·) can be used to predict the corresponding label,f (x), of a new instance x. From a probabilistic viewpoint,
f (x) can be written as P (y|x). In our document classification
example, Y is the set of all labels, which is True, False for abinary classification task, and yi is “True” or “False”.
For simplicity, in this survey, we only consider the casewhere there is one source domain DS , and one target domain,
DT , as this is by far the most popular of the research works in
the literature. More specifically, we denote the source domain
data as DS = {(xS1 , yS1 ), . . . , (xSnS , ySnS )}, where xSi ∈XS is the data instance and ySi ∈ YS is the correspondingclass label. In our document classification example, DS canbe a set of term vectors together with their associated true or
false class labels. Similarly, we denote the target domain data
as DT = {(xT1 , yT1 ), . . . , (xTnT , yTnT )}, where the input xTi
is in XT and yTi ∈ YT is the corresponding output. In mostcases, 0 ≤ nT  nS .
We now give a unified definition of transfer learning.
Definition 1 (Transfer Learning) Given a source domain DS
and learning task TS , a target domain DT and learning task
TT , transfer learning aims to help improve the learning of thetarget predictive function fT (·) in DT using the knowledge in
DS and TS , where DS = DT , or TS = TT .
In the above definition, a domain is a pair D = {X , P (X)}.
Thus the condition DS = DT implies that either XS = XT or
PS (X) = PT (X). For example, in our document classification
example, this means that between a source document set anda target document set, either the term features are different
between the two sets (e.g., they use different languages), or
their marginal distributions are different.
Similarly, a task is defined as a pair T = {Y, P (Y |X)}.
Thus the condition TS = TT implies that either YS = YTor P (YS |XS ) = P (YT |XT ). When the target and source
domains are the same, i.e. DS = DT , and their learning tasks
are the same, i.e., TS = TT , the learning problem becomesa traditional machine learning problem. When the domains
are different, then either (1) the feature spaces between thedomains are different, i.e. XS = XT , or (2) the feature
spaces between the domains are the same but the marginal
probability distributions between domain data are different;
i.e. P (XS ) = P (XT ), where XSi ∈ XS and XTi ∈ XT .
As an example, in our document classification example, case(1) corresponds to when the two sets of documents aredescribed in different languages, and case (2) may correspondto when the source domain documents and the target domain
documents focus on different topics.

Given specific domains DS and DT , when the learning
tasks TS and TT are different, then either (1) the label spaces
between the domains are different, i.e. YS = YT , or (2) the
conditional probability distributions between the domains aredifferent; i.e. P (YS |XS ) = P (YT |XT ), where YSi ∈ YS andYTi ∈ YT . In our document classification example, case (1)corresponds to the situation where source domain has binary
document classes, whereas the target domain has ten classes toclassify the documents to. Case (2) corresponds to the situation
where the source and target documents are very unbalanced
in terms of the user-defined classes.
In addition, when there exists some relationship, explicit or
implicit, between the feature spaces of the two domains, we
say that the source and target domains are related.
2.3 A Categorization of Transfer Learning Techniques
In transfer learning, we have the following three main researchissues: (1) What to transfer; (2) How to transfer; (3) When totransfer.
“What to transfer” asks which part of knowledge canbe transferred across domains or tasks. Some knowledge is
specific for individual domains or tasks, and some knowledge
may be common between different domains such that they may
help improve performance for the target domain or task. Afterdiscovering which knowledge can be transferred, learningalgorithms need to be developed to transfer the knowledge,
which corresponds to the “how to transfer” issue.
“When to transfer” asks in which situations, transferringskills should be done. Likewise, we are interested in knowing
in which situations, knowledge should not be transferred. In
some situations, when the source domain and target domain are
not related to each other, brute-force transfer may be unsuccessful. In the worst case, it may even hurt the performanceof learning in the target domain, a situation which is often
referred to as negative transfer. Most current work on transfer
learning focuses on “What to transfer” and “How to transfer”,
by implicitly assuming that the source and target domains be
related to each other. However, how to avoid negative transfer
is an important open issue that is attracting more and more
attention in the future.
Based on the definition of transfer learning, we summarize
the relationship between traditional machine learning and various transfer learning settings in Table 1, where we categorize
transfer learning under three sub-settings, inductive transfer learning, transductive transfer learning and unsupervisedtransfer learning, based on different situations between thesource and target domains and tasks.
1) In the inductive transfer learning setting, the target task
is different from the source task, no matter when thesource and target domains are the same or not.
In this case, some labeled data in the target domain are
required to induce an objective predictive model fT (·)
for use in the target domain. In addition, according todifferent situations of labeled and unlabeled data in thesource domain, we can further categorize the inductive
transfer learning setting into two cases:

This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
4TABLE 1
Relationship between Traditional Machine Learning and Various Transfer Learning Settings
Learning Settings
Traditional Machine Learning
Inductive Transfer Learning /Transfer Learning
Unsupervised Transfer Learning
Transductive Transfer Learning(1.1) A lot of labeled data in the source domain areavailable. In this case, the inductive transfer learning
setting is similar to the multi-task learning setting.
However, the inductive transfer learning setting only
aims at achieving high performance in the target task
by transferring knowledge from the source task while
multi-task learning tries to learn the target and sourcetask simultaneously.
(1.2) No labeled data in the source domain are available.
In this case, the inductive transfer learning setting issimilar to the self-taught learning setting, which is
first proposed by Raina et al. [22]. In the self-taught
learning setting, the label spaces between the source
and target domains may be different, which implies
the side information of the source domain cannot be
used directly. Thus, it’s similar to the inductive transfer
learning setting where the labeled data in the source
domain are unavailable.
2) In the transductive transfer learning setting, the source
and target tasks are the same, while the source and target
domains are different.
In this situation, no labeled data in the target domain
are available while a lot of labeled data in the source
domain are available. In addition, according to different
situations between the source and target domains, we
can further categorize the transductive transfer learning
setting into two cases.
(2.1) The feature spaces between the source and target
domains are different, XS = XT .
(2.2) The feature spaces between domains are the same,
XS = XT , but the marginal probability distributions of
the input data are different, P (XS ) = P (XT ).
The latter case of the transductive transfer learning
setting is related to domain adaptation for knowledgetransfer in text classification [23] and sample selection
bias [24] or co-variate shift [25], whose assumptions are
similar.
3) Finally, in the unsupervised transfer learning setting,similar to inductive transfer learning setting, the target
task is different from but related to the source task.
However, the unsupervised transfer learning focus on
solving unsupervised learning tasks in the target domain,such as clustering, dimensionality reduction and density
estimation [26], [27]. In this case, there are no labeleddata available in both source and target domains in
training.
The relationship between the different settings of transfer
learning and the related areas are summarized in Table 2 andFigure 2.

Source and Target Domains
the same
the same
different but relateddifferent but related

Source and Target Tasks
the same
different but relateddifferent but related
the same

Approaches to transfer learning in the above three different
settings can be summarized into four cases based on “What totransfer”. Table 3 shows these four cases and brief description.
The first context can be referred to as instance-based transferlearning (or instance-transfer) approach [6], [28], [29], [30],[31], [24], [32], [33], [34], [35], which assumes that certain
parts of the data in the source domain can be reused forlearning in the target domain by re-weighting. Instance reweighting and importance sampling are two major techniquesin this context.
A second case can be referred to as feature-representationtransfer approach [22], [36], [37], [38], [39], [8], [40], [41],[42], [43], [44]. The intuitive idea behind this case is to learn
a “good” feature representation for the target domain. In thiscase, the knowledge used to transfer across domains is encoded
into the learned feature representation. With the new feature
representation, the performance of the target task is expected
to improve significantly.
A third case can be referred to as parameter-transfer approach [45], [46], [47], [48], [49], which assumes that thesource tasks and the target tasks share some parameters or
prior distributions of the hyper-parameters of the models. The
transferred knowledge is encoded into the shared parameters
or priors. Thus, by discovering the shared parameters or priors,
knowledge can be transferred across tasks.
Finally, the last case can be referred to as the relationalknowledge-transfer problem [50], which deals with transfer
learning for relational domains. The basic assumption behind
this context is that some relationship among the data in thesource and target domains are similar. Thus, the knowledge
to be transferred is the relationship among the data. Recently,
statistical relational learning techniques dominate this context[51], [52].
Table 4 shows the cases where the different approaches
are used for each transfer learning setting. We can see thatthe inductive transfer learning setting has been studied inmany research works, while the unsupervised transfer learning
setting is a relatively new research topic and only studied
in the context of the feature-representation-transfer case. In
addition, the feature-representation-transfer problem has been
proposed to all three settings of transfer learning. However,the parameter-transfer and the relational-knowledge-transfer
approach are only studied in the inductive transfer learning
setting, which we discuss in detail below.

3

I NDUCTIVE T RANSFER L EARNING

Definition 2 (Inductive Transfer Learning) Given a source
domain DS and a learning task TS , a target domain DT andThis article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
5TABLE 2Different Settings of Transfer Learning
Transfer Learning Settings
Inductive Transfer Learning

Transductive Transfer Learning

Related Areas
Multi-task Learning

Source Domain Labels
Available

Target Domain Labels
Available

Self-taught Learning

Unavailable

Available

Domain Adaptation, Sample
Selection Bias, Co-variate Shift

Available

Unavailable

Unavailable

Unavailable

Unsupervised Transfer Learning

Tasks
Regression,
Classification
Regression,
Classification
Regression,
Classification
Clustering,
Dimensionality
Reduction

Fig. 2. An Overview of Different Settings of Transfer
TABLE 3Different Approaches to Transfer Learning
Transfer Learning Approaches
Instance-transfer
Feature-representation-transfer
Parameter-transfer
Relational-knowledge-transfer

Brief Description
To re-weight some labeled data in the source domain for use in the target domain [6], [28], [29],[30], [31], [24], [32], [33], [34], [35].
Find a “good” feature representation that reduces difference between the source and the target
domains and the error of classification and regression models [22], [36], [37], [38], [39], [8],[40], [41], [42], [43], [44].
Discover shared parameters or priors between the source domain and target domain models, which
can benefit for transfer learning [45], [46], [47], [48], [49].
Build mapping of relational knowledge between the source domain and the target domains. Bothdomains are relational domains and i.i.d assumption is relaxed in each domain [50], [51], [52].

TABLE 4
Different Approaches Used in Different Settings
Instance-transfer
Feature-representation-transfer
Parameter-transfer
Relational-knowledge-transfer

Inductive
Transfer Learning√√√√

Transductive
Transfer Learning√√

Unsupervised Transfer Learning√

This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
6

a learning task TT , inductive transfer learning aims to help
improve the learning of the target predictive function fT (·) in
DT using the knowledge in DS and TS , where TS = TT .
Based on the above definition of the inductive transfer
learning setting, a few labeled data in the target domain are
required as the training data to induce the target predictivefunction. As mentioned in Section 2.3, this setting has twocases: (1) Labeled data in the source domain are available;(2) Labeled data in the source domain are unavailable while
unlabeled data in the source domain are available. Most
transfer learning approaches in this setting focus on the formercase.
3.1 Transferring Knowledge of Instances
The instance-transfer approach to the inductive transfer learning setting is intuitively appealing: although the source domain
data cannot be reused directly, there are certain parts of thedata that can still be reused together with a few labeled datain the target domain.
Dai et al. [6] proposed a boosting algorithm, TrAdaBoost,which is an extension of the AdaBoost algorithm, to address
the inductive transfer learning problems. TrAdaBoost assumes
that the source and target domain data use exactly the same setof features and labels, but the distributions of the data in thetwo domains are different. In addition, TrAdaBoost assumes
that, due to the difference in distributions between the source
and the target domains, some of the source domain data may
be useful in learning for the target domain but some of them
may not and could even be harmful. It attempts to iteratively
re-weight the source domain data to reduce the effect of the“bad” source data while encourage the “good” source datato contribute more for the target domain. For each round of
iteration, TrAdaBoost trains the base classifier on the weighted
source and target data. The error is only calculated on thetarget data. Furthermore, TrAdaBoost uses the same strategy as
AdaBoost to update the incorrectly classified examples in thetarget domain while using a different strategy from AdaBoost
to update the incorrectly classified source examples in thesource domain. Theoretical analysis of TrAdaBoost in also
given in [6].
Jiang and Zhai [30] proposed a heuristic method to remove“misleading” training examples from the source domain basedon the difference between conditional probabilities P (yT |xT )and P (yS |xS ). Liao et al. [31] proposed a new active learning
method to select the unlabeled data in a target domain to
be labeled with the help of the source domain data. Wu andDietterich [53] integrated the source domain (auxiliary) data an
SVM framework for improving the classification performance.
3.2 Transferring Knowledge of Feature Representations
The feature-representation-transfer approach to the inductive
transfer learning problem aims at finding “good” feature
representations to minimize domain divergence and classification or regression model error. Strategies to find “good”
feature representations are different for different types of thesource domain data. If a lot of labeled data in the source
domain are available, supervised learning methods can beused to construct a feature representation. This is similar tocommon feature learning in the field of multi-task learning[40]. If no labeled data in the source domain are available,
unsupervised learning methods are proposed to construct thefeature representation.
3.2.1 Supervised Feature Construction
Supervised feature construction methods for the inductive
transfer learning setting are similar to those used in multitask learning. The basic idea is to learn a low-dimensional
representation that is shared across related tasks. In addition,
the learned new representation can reduce the classification
or regression model error of each task as well. Argyriou etal. [40] proposed a sparse feature learning method for multitask learning. In the inductive transfer learning setting, thecommon features can be learned by solving an optimization
problem, given as follows.
nt 

arg min
A,Ut∈{T,S} i=1

s.t.

U ∈ OdL(yti , at , U T xti ) + γA22,1 (1)In this equation, S and T denote the tasks in the source
domain and target domain, respectively. A = [aS , aT ] ∈ Rd×2
is a matrix of parameters. U is a d × d orthogonal matrix(mapping function) for mapping the original high-dimensional
data to low-dimensional representations. The (r, p)-norm ofd1
A is defined as Ar,p := ( i=1 ai pr ) p . The optimization
problem (1) estimates the low-dimensional representations
U T XT , U T XS and the parameters, A, of the model at thesame time. The optimization problem (1) can be further transformed into an equivalent convex optimization formulation andbe solved efficiently. In a follow-up work, Argyriou et al. [41]proposed a spectral regularization framework on matrices for
multi-task structure learning.
Lee et al. [42] proposed a convex optimization algorithm forsimultaneously learning meta-priors and feature weights froman ensemble of related prediction tasks. The meta-priors canbe transferred among different tasks. Jebara [43] proposed to
select features for multi-task learning with SVMs. Ruckert etal. [54] designed a kernel-based approach to inductive transfer,which aims at finding a suitable kernel for the target data.
3.2.2 Unsupervised Feature Construction
In [22], Raina et al. proposed to apply sparse coding [55],which is an unsupervised feature construction method, forlearning higher level features for transfer learning. The basic
idea of this approach consists of two steps. In the first step,higher-level basis vectors b = {b1 , b2 , . . . , bs } are learned on
the source domain data by solving the optimization problem(2) as shown as follows,xSi − j ajSi bj 22 + βaSi 1
(2)
min
a,b

i

s.t.

bj 2 ≤ 1, ∀j ∈ 1, . . . , sThis article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
7

In this equation, ajSi is a new representation of basis bj for
input xSi and β is a coefficient to balance the feature construction term and the regularization term. After learning thebasis vectors b, in the second step, an optimization algorithm(3) is applied on the target domain data to learn higher levelfeatures based on the basis vectors b.
 ja∗Ti = arg min xTi −aTi bj 22 + βaTi 1
(3)
aTi

j

Finally, discriminative algorithms can be applied to {a∗Ti } swith corresponding labels to train classification or regression
models for use in the target domain. One drawback of thismethod is that the so-called higher-level basis vectors learnedon the source domain in the optimization problem (2) may not
be suitable for use in the target domain.
Recently, manifold learning methods have been adapted fortransfer learning. In [44], Wang and Mahadevan proposed
a Procrustes analysis based approach to manifold alignment
without correspondences, which can be used to transfer theknowledge across domains via the aligned manifolds.
3.3 Transferring Knowledge of Parameters
Most parameter-transfer approaches to the inductive transfer
learning setting assume that individual models for related tasks
should share some parameters or prior distributions of hyperparameters. Most approaches described in this section, including a regularization framework and a hierarchical Bayesianframework, are designed to work under multi-task learning.
However, they can be easily modified for transfer learning.
As mentioned above, multi-task learning tries to learn boththe source and target tasks simultaneously and perfectly, whiletransfer learning only aims at boosting the performance ofthe target domain by utilizing the source domain data. Thus,in multi-task learning, weights of the loss functions for thesource and target data are the same. In contrast, in transfer
learning, weights in the loss functions for different domains
can be different. Intuitively, we may assign a larger weight tothe loss function of the target domain to make sure that we
can achieve better performance in the target domain.
Lawrence and Platt [45] proposed an efficient algorithm
known as MT-IVM, which is based on Gaussian Processes(GP), to handle the multi-task learning case. MT-IVM tries tolearn parameters of a Gaussian Process over multiple tasks bysharing the same GP prior. Bonilla et al. [46] also investigatedmulti-task learning in the context of GP. The authors proposed
to use a free-form covariance matrix over tasks to modelinter-task dependencies, where a GP prior is used to induce
correlations between tasks. Schwaighofer et al. [47] proposed
to use a hierarchical Bayesian framework (HB) together with
GP for multi-task learning.
Besides transferring the priors of the GP models, someresearchers also proposed to transfer parameters of SVMs
under a regularization framework. Evgeniou and Pontil [48]borrowed the idea of HB to SVMs for multi-task learning.
The proposed method assumed that the parameter, w, in SVMs
for each task can be separated into two terms. One is acommon term over tasks and the other is a task-specific term.

In inductive transfer learning,wS = w0 + vS and wT = w0 + vT ,where, wS and wT are parameters of the SVMs for the sourcetask and the target learning task, respectively. w0 is a common
parameter while vS and vT are specific parameters for thesource task and the target task, respectively. By assuming ft =wt · x to be a hyper-plane for task t, an extension of SVMs
to multi-task learning case can be written as the following:min

J(w0 , vt , ξti )w0 ,vt ,ξti

=

nt
 
t∈{S,T } i=1

ξti +

(4)
λ1
2



vt 2 + λ2 w0 2t∈{S,T }s.t. yti (w0 + vt ) · xti ≥ 1 − ξti ,ξti ≥ 0, i ∈ {1, 2, ..., nt } and t ∈ {S, T }.
By solving the optimization problem above, we can learn theparameters w0 , vS and vT simultaneously.
Several researchers have pursued the parameter transfer
approach further. Gao et al. [49] proposed a locally weighted
ensemble learning framework to combine multiple models fortransfer learning, where the weights are dynamically assigned
according to a model’s predictive power on each test example
in the target domain.
3.4 Transferring Relational Knowledge
Different from other three contexts, the relational-knowledgetransfer approach deals with transfer learning problems in
relational domains, where the data are non-i.i.d. and can be
represented by multiple relations, such as networked data andsocial network data. This approach does not assume that thedata drawn from each domain be independent and identically
distributed (i.i.d.) as traditionally assumed. It tries to transfer
the relationship among data from a source domain to atarget domain. In this context, statistical relational learningtechniques are proposed to solve these problems.
Mihalkova et al. [50] proposed an algorithm TAMAR thattransfers relational knowledge with Markov Logic Networks(MLNs) across relational domains. MLNs [56] is a powerfulformalism, which combines the compact expressiveness offirst order logic with flexibility of probability, for statistical
relational learning. In MLNs, entities in a relational domain
are represented by predicates and their relationships are represented in first-order logic. TAMAR is motivated by the factthat if two domains are related to each other, there may exist
mappings to connect entities and their relationships from asource domain to a target domain. For example, a professor
can be considered as playing a similar role in an academic
domain as a manager in an industrial management domain.
In addition, the relationship between a professor and his or
her students is similar to the relationship between a manager
and his or her workers. Thus, there may exist a mappingfrom professor to manager and a mapping from the professorstudent relationship to the manager-worker relationship. In thisvein, TAMAR tries to use an MLN learned for a source domain
to aid in the learning of an MLN for a target domain. Basically,

This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
8

TAMAR is a two-stage algorithm. In the first step, a mapping
is constructed from a source MLN to the target domain basedon weighted pseudo loglikelihood measure (WPLL). In thesecond step, a revision is done for the mapped structure in thetarget domain through the FORTE algorithm [57], which is aninductive logic programming (ILP) algorithm for revising firstorder theories. The revised MLN can be used as a relationalmodel for inference or reasoning in the target domain.
In the AAAI-2008 workshop on transfer learning for complex tasks 4 , Mihalkova et al. [51] extended TAMAR to thesingle-entity-centered setting of transfer learning, where only
one entity in a target domain is available. Davis et al. [52]proposed an approach to transferring relational knowledge
based on a form of second-order Markov logic. The basic
idea of the algorithm is to discover structural regularities inthe source domain in the form of Markov logic formulaswith predicate variables, by instantiating these formulas withpredicates from the target domain.

4

T RANSDUCTIVE T RANSFER L EARNING

The term transductive transfer learning was first proposed
by Arnold et al. [58], where they required that the source
and target tasks be the same, although the domains may bedifferent. On top of these conditions, they further requiredthat that all unlabeled data in the target domain are available
at training time, but we believe that this condition can berelaxed; instead, in our definition of the transductive transfer
learning setting, we only require that part of the unlabeled
target data be seen at training time in order to obtain themarginal probability for the target data.
Note that the word ’transductive’ is used with several meanings. In the traditional machine learning setting, transductive
learning [59] refers to the situation where all test data are
required to be seen at training time, and that the learned modelcannot be reused for future data. Thus, when some new test
data arrive, they must be classified together with all existing
data. In our categorization of transfer learning, in contrast, we
use the term transductive to emphasize the concept that in thistype of transfer learning, the tasks must be the same and there
must be some unlabeled data available in the target domain.
Definition 3 (Transductive Transfer Learning) Given a source
domain DS and a corresponding learning task TS , a target
domain DT and a corresponding learning task TT , transductive
transfer learning aims to improve the learning of the target
predictive function fT (·) in DT using the knowledge in DS
and TS , where DS = DT and TS = TT . In addition, someunlabeled target domain data must be available at training time.
This definition covers the work of Arnold et al. [58], since
the latter considered domain adaptation, where the difference
lies between the marginal probability distributions of source
and target data; i.e., the tasks are the same but the domains
are different.
Similar to the traditional transductive learning setting, whichaims to make the best use of the unlabeled test data for learning, in our classification scheme under transductive transfer
4. http://www.cs.utexas.edu/∼mtaylor/AAAI08TL/

learning, we also assume that some target-domain unlabeleddata be given. In the above definition of transductive transfer
learning, the source and target tasks are the same, which
implies that one can adapt the predictive function learnedin the source domain for use in the target domain throughsome unlabeled target-domain data. As mentioned in Section
2.3, this setting can be split to two cases: (a) The feature
spaces between the source and target domains are different,
XS = XT , and (b) the feature spaces between domains are thesame, XS = XT , but the marginal probability distributions of
the input data are different, P (XS ) = P (XT ). This is similar
to the requirements in domain adaptation and sample selection
bias. Most approaches described in the following sections are
related to case (b) above.
4.1 Transferring the Knowledge of Instances
Most instance-transfer approaches to the transductive transfer
learning setting are motivated by importance sampling. To
see how importance sampling based methods may help inthis setting, we first review the problem of empirical risk
minimization (ERM) [60]. In general, we might want to learn
the optimal parameters θ∗ of the model by minimizing the
expected risk,θ∗ = arg min E(x,y)∈P [l(x, y, θ)],θ∈Θwhere l(x, y, θ) is a loss function that depends on the parameter θ. However, since it is hard to estimate the probability
distribution P , we choose to minimize the ERM instead,n1θ∗ = arg min[l(xi , yi , θ)],
n i=1
θ∈Θ
where n is size of the training data.
In the transductive transfer learning setting, we want tolearn an optimal model for the target domain by minimizing
the expected risk,θ∗ = arg min
P (DT )l(x, y, θ).
θ∈Θ(x,y)∈DT

However, since no labeled data in the target domain are
observed in training data, we have to learn a model from thesource domain data instead. If P (DS ) = P (DT ), then we may
simply learn the model by solving the following optimization
problem for use in the target domain,θ∗ = arg min
P (DS )l(x, y, θ).
θ∈Θ(x,y)∈DS

Otherwise, when P (DS ) = P (DT ), we need to modify
the above optimization problem to learn a model with highgeneralization ability for the target domain, as follows: P (DT )P (DS )l(x, y, θ)θ∗ = arg min
P (DS )
θ∈Θ≈ arg minθ∈Θ(x,y)∈DSnS
PT (xTi , yTi )i=1

PS (xSi , ySi )l(xSi , ySi , θ).

(5)Therefore, by adding different penalty values to each instanceP (x ,y )(xSi , ySi ) with the corresponding weight PTS (xSTi ,ySTi ) , we caniiThis article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
9learn a precise model for the target domain. Furthermore,since P (YT |XT ) = P (YS |XS ). Thus the difference between
P (DS ) and P (DT ) is caused by P (XS ) and P (XT ) andP (xSi )PT (xTi ,yTi )P (xSi )PS (xSi ,ySi ) = P (xTi ) . If we can estimate P (xTi ) for each
instance, we can solve the transductive transfer learning
problems.
P (x )
There exist various ways to estimate P (xSTi ) . Zadroznyi[24] proposed to estimate the terms P (xSi ) and P (xTi )independently by constructing simple classification problems.
Fan et al. [35] further analyzed the problems by using various
classifiers to estimate the probability ratio. Huang et al. [32]proposed a kernel-mean matching (KMM) algorithm to learn
P (xSi )P (xTi ) directly by matching the means between the source
domain data and the target domain data in a reproducingkernel Hilbert space (RKHS). KMM can be rewritten as thefollowing quadratic programming (QP) optimization problem.
1 TT
2 β Kβ − κ β

min
β

(6)

nS

s.t. βi ∈ [0, B] and | i=1 βi − nS | ≤ nS 
KS,S KS,T
and Kij = k(xi , xj ). KS,S andwhere K = K
K
T,S

T,T

KT,T are kernel matrices for the source domain
data and thenS nTtarget domain data,
respectively.
κ=k(xi , xTj ),
i
j=1
n
T

where xi ∈ XS XT , while xTj ∈ XT .
P (x )
It can be proved that βi = P (xSTi ) [32]. An advantage ofiusing KMM is that it can avoid performing density estimationof either P (xSi ) or P (xTi ), which is difficult when the sizeof the data set is small. Sugiyama et al. [34] proposed analgorithm known as Kullback-Leibler Importance Estimation
P (x )
Procedure (KLIEP) to estimate P (xSTi ) directly, based on theiminimization of the Kullback-Leibler divergence. KLIEP canbe integrated with cross-validation to perform model selection automatically in two steps: (1) estimating the weightsof the source domain data; (2) training models on the reweighted data. Bickel et al. [33] combined the two steps ina unified framework by deriving a kernel-logistic regression
classifier. Besides sample re-weighting techniques, Dai etal. [28] extended a traditional Naive Bayesian classifier for thetransductive transfer learning problems. For more information
on importance sampling and re-weighting methods for covariate shift or sample selection bias, readers can refer to a
recently published book [29] by Quionero-Candela et al. One
can also consult a tutorial on Sample Selection Bias by Fan
and Sugiyama in ICDM-08 5 .
4.2 Transferring Knowledge of Feature Representations
Most feature-representation transfer approaches to the transductive transfer learning setting are under unsupervised learning frameworks. Blitzer et al. [38] proposed a structural correspondence learning (SCL) algorithm, which extends [37],to make use of the unlabeled data from the target domain to
extract some revelent features that may reduce the difference
5. Tutorial slides can be foundfan/PPT/ICDM08SampleBias.ppt

at

between the domains. The first step of SCL is to define a setof pivot features 6 (the number of pivot feature is denotedby m) on the unlabeled data from both domains. Then, SCL
removes these pivot features from the data and treats each pivot
feature as a new label vector. The m classification problems
can be constructed. By assuming each problem can be solved
by linear classifier, which is shown as follows,
fl (x) = sgn(wlT · x), l = 1, . . . , m
SCL can learn a matrix W = [w1 w2 . . . wm ] of parameters. In
the third step, singular value decomposition (SVD) is applied
to matrix W = [w1 w2 . . . wm ]. Let W = U DV T , thenT
θ = U[1:h,:](h is the number of the shared features) is thematrix (linear mapping) whose rows are the top left singular
vectors of W . Finally, standard discriminative algorithms canbe applied to the augmented feature vector to build models.
The augmented feature vector contains all the original feature
xi appended with the new shared features θxi . As mentionedin [38], if the pivot features are well designed, then thelearned mapping θ encodes the correspondence between thefeatures from the different domains. Although Ben-David and
Schuller [61] showed experimentally that SCL can reduce thedifference between domains, how to select the pivot features
is difficult and domain-dependent. In [38], Blitzer et al. used aheuristic method to select pivot features for natural language
processing (NLP) problems, such as tagging of sentences. In
their follow-up work, the researchers proposed to use Mutual
Information (MI) to choose the pivot features instead of using
more heuristic criteria [8]. MI-SCL tries to find some pivot
features that have high dependence on the labels in the source
domain.
Transfer learning in the NLP domain is sometimes referred to as domain adaptation. In this area, Daumé [39]proposed a kernel-mapping function for NLP problems, whichmaps the data from both source and target domains to ahigh-dimensional feature space, where standard discriminative
learning methods are used to train the classifiers. However,the constructed kernel mapping function is domain knowledge
driven. It is not easy to generalize the kernel mapping to otherareas or applications. Blitzer et al. [62] analyzed the uniform
convergence bounds for algorithms that minimized a convex
combination of source and target empirical risks.
In [36], Dai et al. proposed a co-clustering based algorithm
to propagate the label information across different domains.
In [63], Xing et al. proposed a novel algorithm known asbridged refinement to correct the labels predicted by a shiftunaware classifier towards a target distribution and take themixture distribution of the training and test data as a bridge tobetter transfer from the training data to the test data. In [64],Ling et al. proposed a spectral classification framework forcross-domain transfer learning problem, where the objectivefunction is introduced to seek consistency between the indomain supervision and the out-of-domain intrinsic structure.
In [65], Xue et al. proposed a cross-domain text classification
algorithm that extended the traditional probabilistic latentsemantic analysis (PLSA) algorithm to integrate labeled andhttp://www.cs.columbia.edu/∼
6. The pivot features are domain specific and depend on prior knowledge

This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
10

unlabeled data from different but related domains, into a
unified probabilistic model. The new model is called Topicbridged PLSA, or TPLSA.
Transfer learning via dimensionality reduction was recently proposed by Pan et al. [66]. In this work, Pan etal. exploited the Maximum Mean Discrepancy Embedding(MMDE) method, originally designed for dimensionality reduction, to learn a low dimensional space to reduce thedifference of distributions between different domains for transductive transfer learning. However, MMDE may suffer fromits computational burden. Thus, in [67], Pan et al. further
proposed an efficient feature extraction algorithm, known as
Transfer Component Analysis (TCA) to overcome the drawback of MMDE.

5U NSUPERVISED T RANSFER L EARNING

Definition 4 (Unsupervised Transfer Learning) Given asource domain DS with a learning task TS , a target domain DT
and a corresponding learning task TT , unsupervised transfer
learning aims to help improve the learning of the target
predictive function fT (·) 7 in DT using the knowledge in DS
and TS , where TS = TT and YS and YT are not observable.
Based on the definition of the unsupervised transfer learning setting, no labeled data are observed in the source andtarget domains in training. So far, there is little research workon this setting. Recently, Self-taught clustering (STC) [26]and transferred discriminative analysis (TDA) [27] algorithms
are proposed to transfer clustering and transfer dimensionality
reduction problems, respectively.
5.1 Transferring Knowledge of Feature Representations
Dai et al. [26] studied a new case of clustering problems,
known as self-taught clustering. Self-taught clustering is an
instance of unsupervised transfer learning, which aims atclustering a small collection of unlabeled data in the target
domain with the help of a large amount of unlabeled data inthe source domain. STC tries to learn a common feature spaceacross domains, which helps in clustering in the target domain.
The objective function of STC is shown as follows.
J(X˜T , X˜S , Z̃)(7)= I(XT , Z) − I(X˜T , Z̃) + λ I(XS , Z) − I(X˜S , Z̃)where XS and XT are the source and target domain data,
respectively. Z is a shared feature space by XS and XT ,and I(·, ·) is the mutual information between two randomvariables. Suppose that there exist three clustering functions
CXT : XT → X˜T , CXS : XS → X˜S and CZ : Z → Z̃, whereX˜T , X˜S and Z̃ are corresponding clusters of XT , XS and Z,
respectively. The goal of STC is to learn X˜T by solving the
optimization problem (7):arg min J(X˜T , X˜S , Z̃)

X˜T ,X˜S ,Z̃(8)

7. In unsupervised transfer learning, the predicted labels are latent variables,such as clusters or reduced dimensions

An iterative algorithm for solving the optimization function(8) was given in [26].
Similarly, [27] proposed a transferred discriminative analysis (TDA) algorithm to solve the transfer dimensionality
reduction problem. TDA first applies clustering methods togenerate pseudo-class labels for the target unlabeled data. It
then applies dimensionality reduction methods to the target
data and labeled source data to reduce the dimensions. These
two steps run iteratively to find the best subspace for the target
data.

6 T RANSFERT RANSFERB OUNDSANDN EGATIVE

An important issue is to recognize the limit of the power oftransfer learning. In [68], Hassan Mahmud and Ray analyzed
the case of transfer learning using Kolmogorov complexity,
where some theoretical bounds are proved. In particular, theauthors used conditional Kolmogorov complexity to measure
relatedness between tasks and transfer the “right” amountof information in a sequential transfer learning task under aBayesian framework.
Recently, Eaton et al. [69] proposed a novel graph-based
method for knowledge transfer, where the relationships between source tasks are modeled by embedding the set oflearned source models in a graph using transferability as themetric. Transferring to a new task proceeds by mapping the
problem into the graph and then learning a function on thisgraph that automatically determines the parameters to transfer
to the new learning task.
Negative transfer happens when the source domain data andtask contribute to the reduced performance of learning in thetarget domain. Despite the fact that how to avoid negativetransfer is a very important issue, little research work has
been published on this topic. Rosenstein et al. [70] empiricallyshowed that if two tasks are too dissimilar, then brute-force
transfer may hurt the performance of the target task. Someworks have been exploited to analyze relatedness among tasks
and task clustering techniques, such as [71], [72], which
may help provide guidance on how to avoid negative transfer
automatically. Bakker and Heskes [72] adopted a Bayesian
approach in which some of the model parameters are sharedfor all tasks and others more loosely connected through a joint
prior distribution that can be learned from the data. Thus,
the data are clustered based on the task parameters, wheretasks in the same cluster are supposed to be related to eachothers. Argyriou et al. [73] considered situations in which thelearning tasks can be divided into groups. Tasks within eachgroup are related by sharing a low-dimensional representation,
which differs among different groups. As a result, tasks withina group can find it easier to transfer useful knowledge.

7

A PPLICATIONS OF T RANSFER L EARNING

Recently, transfer learning techniques have been applied successfully in many real-world applications. Raina et al. [74] andDai et al. [36], [28] proposed to use transfer learning techniques to learn text data across domains, respectively. Blitzer
et al. [38] proposed to use SCL for solving NLP problems. An

This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
11

extension of SCL was proposed in [8] for solving sentimentclassification problems. Wu and Dietterich [53] proposed touse both inadequate target domain data and plenty of low
quality source domain data for image classification problems.
Arnold et al. [58] proposed to use transductive transfer learning methods to solve name-entity recognition problems. In[75], [76], [77], [78], [79], transfer learning techniques areproposed to extract knowledge from WiFi localization models
across time periods, space and mobile devices, to benefit WiFi
localization tasks in other settings. Zhuo et al. [80] studied how
to transfer domain knowledge to learn relational action models
across domains in automated planning.
In [81], Raykar et al. proposed a novel Bayesian multipleinstance learning algorithm, which can automatically identify
the relevant feature subset and use inductive transfer forlearning multiple, but conceptually related, classifiers, forcomputer aided design (CAD). In [82], Ling et al. proposed an
information-theoretic approach for transfer learning to address
the cross-language classification problem for translating Web
pages from English to Chinese. The approach addressed the
problem when there are plenty of labeled English text data
whereas there are only a small number of labeled Chinese text
documents. Transfer learning across the two feature spaces areachieved by designing a suitable mapping function as a bridge.
So far, there are at least two international competitions basedon transfer learning, which made available some much needed
public data. In the ECML/PKDD-2006 discovery challenge8, the task was to handle personalized spam filtering andgeneralization across related learning tasks. For training a
spam-filtering system, we need to collect a lot of emails froma group of users with corresponding labels: spam or not spam,and train a classifier based on these data. For a new email user,
we might want to adapt the learned model for the user. The
challenge is that the distributions of emails for the first set ofusers and the new user are different. Thus, this problem canbe modeled as an inductive transfer learning problem, whichaims to adapt an old spam-filtering model to a new situation
with fewer training data and less training time.
A second data set was made available through the ICDM2007 Contest, in which a task was to estimate a WiFi client’s
indoor locations using the WiFi signal data obtained overdifferent periods of time [83]. Since the values of WiFi
signal strength may be a function of time, space and devices,
distributions of WiFi data over different time periods may bevery different. Thus, transfer learning must be designed toreduce the data re-labeling effort.
Data Sets for Transfer Learning: So far, several data
sets have been published for transfer learning research. We
denote the text mining data sets, Email spam-filtering dataset, the WiFi localization over time periods data set and theSentiment classification data set by Text, Email, WiFi andSen, respectively.
Text Three data sets, 20 Newsgroups, SRAA and Reuters21578 9 , have been preprocessed for a transfer learning setting by some researchers. The data in these
8. http://www.ecmlpkdd2006.org/challenge.html
9. http://apex.sjtu.edu.cn/apex wiki/dwyak

data sets are categorized to a hierarchical structure.
Data from different sub-categories under the same
parent category are considered to be from different
but related domains. The task is to predict the labelsof the parent category.
Email This data set is provided by the 2006 ECML/PKDD
discovery challenge.
WiFi This data set is provided by the ICDM-2007 Contest
10. The data were collected inside a building for
localization around 145.5 × 37.5 m2 in two different
time periods.
Sen This data set was first used in [8]11 . This data set contains product reviews downloaded from Amazon.com
from 4 product types (domains): Kitchen, Books,
DVDs, and Electronics. Each domain has several
thousand reviews, but the exact number varies bydomain. Reviews contain star ratings (1 to 5 stars).
Empirical Evaluation To show how much benefit transfer
learning methods can bring as compared to traditional learning
methods, researchers have used some public data sets. We
show a list taken from some published transfer learning
papers in Table 5. In [6], [84], [49], the authors used the 20
Newsgroups data 12 as one of the evaluation data sets. Dueto the differences in the preprocessing steps of the algorithms
by different researchers, it is hard to compare the proposed
methods directly. Thus, we denote them by 20-Newsgroups1 ,20-Newsgroups2 and 20-Newsgroups3 , respectively, and show
the comparison results between the proposed transfer learning
methods and non-transfer learning methods in the table.
On the 20 Newsgroups1 data, Dai et al. [6] showed thecomparison experiments between standard Support Vector
Machine (SVM) and the proposed TrAdaBoost algorithm. On
20 Newsgroups2 , Shi et al. [84] applied an active learningalgorithm to select important instances for transfer learning(AcTraK) with TrAdaBoost and standard SVM. Gao et al. [49]evaluated their proposed locally weighted ensemble learningalgorithms, pLWE and LWE, on the 20 Newsgroups3 , compared to SVM and Logistic Regression (LR).
In addition, in the table, we also show the comparison
results on the sentiment classification data set reported in [8].
On this data set, SGD denotes the stochastic gradient-descentalgorithm with Huber loss, SCL represents a linear predictor
on the new representations learned by Structural Correspondence Learning algorithm, and SCL-MI is an extension of SCL
by applying Mutual Information to select the pivot features for
the SCL algorithm.
Finally, on the WiFi localization data set, we show thecomparison results reported in [67], where the baseline is aregularized least square regression model (RLSR), which isa standard regression model, and KPCA, which represents toapply RLSR on the new representations of the data learned by
Kernel Principle Component Analysis. The compared transfer
learning methods include Kernel Mean Matching (KMM) andthe proposed algorithm, Transfer Component Analysis (TCA).
10. http://www.cse.ust.hk/∼qyang/ICDMDMC2007
11. http://www.cis.upenn.edu/∼mdredze/datasets/sentiment/
12. http://people.csail.mit.edu/jrennie/20Newsgroups/

This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
12

For more detail about the experimental results, the readers
may refer to the reference papers showed in the table. Fromthese comparison results, we can find that the transfer learning
methods designed appropriately for real world applications canindeed improve the performance significantly compared to the
non-transfer learning methods.
Toolboxes for Transfer Learning: Researchers at UC Berkeley provided a MATLAB toolkit for transfer learning 13 .
The toolkit contains algorithms and benchmark data sets fortransfer learning. In addition, it provides a standard platform
for developing and testing new algorithms for transfer learning.
7.1 Other Applications of Transfer Learning
Transfer learning has found many applications in sequentialmachine learning as well. For example, [85] proposed a graphbased method for identifying previously encountered games,
and applied this technique to automate domain mapping forvalue function transfer and speed up reinforcement learningon variants of previously played games. A new approach totransfer between entirely different feature spaces is proposedin translated learning, which is made possible by learninga mapping function for bridging features in two entirely
different domains (images and text) [86]. Finally, Li et al. [87],[88] have applied transfer learning to collaborative filtering
problems to solve the cold start and sparsity problems. In [87],Li et al. learned a shared rating-pattern mixture model, known
as a Rating-Matrix Generative Model (RMGM), in terms of
the latent user- and item-cluster variables. RMGM bridges
multiple rating matrices from different domains by mapping
the users and items in each rating matrix onto the shared latent
user and item spaces in order to transfer useful knowledge.
In [88], they applied co-clustering algorithms on users anditems in an auxiliary rating matrix. They then constructed acluster-level rating matrix known as a codebook. By assuming
the target rating matrix (on movies) is related to the auxiliary
one (on books), the target domain can be reconstructed byexpanding the codebook, completing the knowledge transfer
process.

8

C ONCLUSIONSIn this survey article, we have reviewed several current trendsof transfer learning. Transfer learning is classified to threedifferent settings: inductive transfer learning, transductive
transfer learning and unsupervised transfer learning. Most previous works focused on the former two settings. Unsupervisedtransfer learning may attract more and more attention in the
future.
Furthermore, each of the approaches to transfer learning
can be classified into four contexts based on “what to transfer” in learning. They include the instance-transfer approach,the feature-representation-transfer approach, the parametertransfer approach and the relational-knowledge-transfer approach, respectively. The former three contexts have an i.i.d
assumption on the data while the last context deals withtransfer learning on relational data. Most of these approaches
13. http://multitask.cs.berkeley.edu/

assume that the selected source domain is related to the target
domain.
In the future, several important research issues need to beaddressed. First, how to avoid negative transfer is an open
problem. As mentioned in Section 6, many proposed transfer
learning algorithms assume that the source and target domains
are related to each other in some sense. However, if the assumption does not hold, negative transfer may happen, which
may cause the learner to perform worse than no transferring at
all. Thus, how to make sure that no negative transfer happens
is a crucial issue in transfer learning. In order to avoid negativetransfer learning, we need to first study transferability between
source domains or tasks and target domains or tasks. Based onsuitable transferability measures, we can then select relevant
source domains or tasks to extract knowledge from for learning
the target tasks. To define the transferability between domains
and tasks, we also need to define the criteria to measure thesimilarity between domains or tasks. Based on the distancemeasures, we can then cluster domains or tasks, which may
help measure transferability. A related issue is when an entire
domain cannot be used for transfer learning, whether we canstill transfer part of the domain for useful learning in the target
domain.
In addition, most existing transfer learning algorithms so far
focused on improving generalization across different distributions between source and target domains or tasks. In doing so,they assumed that the feature spaces between the source andtarget domains are the same. However, in many applications,
we may wish to transfer knowledge across domains or tasks
that have different feature spaces, and transfer from multiple
such source domains. We refer to this type of transfer learning
as heterogeneous transfer learning.
Finally, so far transfer learning techniques have been mainly
applied to small scale applications with a limited variety, suchas sensor-network-based localization, text classification andimage classification problems. In the future, transfer learningtechniques will be widely used to solve other challenging applications, such as video classification, social network analysis
and logical inference.
Acknowledgment
We thank the support of Hong Kong CERG Project 621307
and a grant from NEC China Lab.

R EFERENCES
[1]

[2]
[3]
[4]

X. Wu, V. Kumar, J. R. Quinlan, J. Ghosh, Q. Yang, H. Motoda, G. J.
McLachlan, A. F. M. Ng, B. Liu, P. S. Yu, Z.-H. Zhou, M. Steinbach,D. J. Hand, and D. Steinberg, “Top 10 algorithms in data mining,”
Knowl. Inf. Syst., vol. 14, no. 1, pp. 1–37, 2008.
Q. Yang and X. Wu, “10 challenging problems in data mining research,”
International Journal of Information Technology and Decision Making,vol. 5, no. 4, pp. 597–604, 2006.
G. P. C. Fung, J. X. Yu, H. Lu, and P. S. Yu, “Text classification without
negative examples revisit,” IEEE Transactions on Knowledge and Data
Engineering, vol. 18, no. 1, pp. 6–20, 2006.
H. Al-Mubaid and S. A. Umair, “A new text categorization techniqueusing distributional clustering and learning logic,” IEEE Transactions
on Knowledge and Data Engineering, vol. 18, no. 9, pp. 1156–1165,
2006.

This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
13

TABLE 5Comparison between transfer learning and non-transfer learning methods
Data Set (reference)
20 Newsgroups1 ([6])
ACC (unit: %)20 Newsgroups2 ([84])ACC (unit: %)20 Newsgroups3 ([49])
ACC (unit: %)Sentiment Classification ([8])
ACC (unit: %)WiFi Localization ([67])
AED (unit: meter)[5]

Source v.s. Target
rec v.s. talkrec v.s. sci
sci v.s. talkrec v.s. talkrec v.s. scicomp v.s. talkcomp v.s. scicomp v.s. recsci v.s. talkcomp v.s. sci
rec v.s. talkrec v.s. sci
sci v.s. talkcomp v.s. reccomp v.s. talk
DVD v.s. bookelectronics v.s. book
kitchen v.s. book
book v.s. DVD
electronics v.s. DVD
kitchen v.s. DVD
book v.s. electronics
DVD v.s. electronics
kitchen v.s. electronics
book v.s. kitchen
DVD v.s. kitchenelectronics v.s. kitchen
Time A v.s. Time B

K. Sarinnapakorn and M. Kubat, “Combining subclassifiers in text categorization: A dst-based solution and a case study,” IEEE Transactions
on Knowledge and Data Engineering, vol. 19, no. 12, pp. 1638–1651,
2007.
[6] W. Dai, Q. Yang, G. Xue, and Y. Yu, “Boosting for transfer learning,” inProceedings of the 24th International Conference on Machine Learning,
Corvalis, Oregon, USA, June 2007, pp. 193–200.
[7] S. J. Pan, V. W. Zheng, Q. Yang, and D. H. Hu, “Transfer learning
for wifi-based indoor localization,” in Proceedings of the Workshop on
Transfer Learning for Complex Task of the 23rd AAAI Conference on
Artificial Intelligence, Chicago, Illinois, USA, July 2008.
[8] J. Blitzer, M. Dredze, and F. Pereira, “Biographies, bollywood, boomboxes and blenders: Domain adaptation for sentiment classification,”in Proceedings of the 45th Annual Meeting of the Association ofComputational Linguistics, Prague, Czech Republic, 2007, pp. 432–439.
[9] J. Ramon, K. Driessens, and T. Croonenborghs, “Transfer learning in
reinforcement learning problems through partial policy recycling,” in
ECML ’07: Proceedings of the 18th European conference on Machine
Learning. Berlin, Heidelberg: Springer-Verlag, 2007, pp. 699–707.
[10] M. E. Taylor and P. Stone, “Cross-domain transfer for reinforcement
learning,” in ICML ’07: Proceedings of the 24th international conference
on Machine learning. New York, NY, USA: ACM, 2007, pp. 879–886.
[11] X. Yin, J. Han, J. Yang, and P. S. Yu, “Efficient classification acrossmultiple database relations: A crossmine approach,” IEEE Transactions
on Knowledge and Data Engineering, vol. 18, no. 6, pp. 770–783, 2006.
[12] L. I. Kuncheva and J. J. Rodrłguez, “Classifier ensembles with a random
linear oracle,” IEEE Transactions on Knowledge and Data Engineering,
vol. 19, no. 4, pp. 500–508, 2007.
[13] E. Baralis, S. Chiusano, and P. Garza, “A lazy approach to associativeclassification,” IEEE Transactions on Knowledge and Data Engineering,
vol. 20, no. 2, pp. 156–171, 2008.
[14] X. Zhu, “Semi-supervised learning literature survey,” University ofBaselines
SVM
87.3%
83.6%
82.3%
SVM
60.2%
59.1%
53.6%
52.7%
49.1%
57.6%
SVM
LR
71.18% 73.49%
68.24% 72.17%
78.16% 78.85%
75.77% 79.04%
81.56% 83.34%
93.89% 91.76%
SGD
72.8%
70.7%
70.9%
77.2%
70.6%
72.7%
70.8%
73.0%
82.7%
74.5%
74.0%
84.0%
RLSR
PCA
6.52
3.16

TL Methods
TrAdaBoost
92.0%
90.3%
87.5%
TrAdaBoost
AcTraK
72.3%
75.4%
67.4%
70.6%
74.4%
80.9%
57.3%
78.0%
77.2%
82.1%
71.3%
75.1%
pLWE
LWE
78.72%
97.44%
72.17%
99.23%
88.45%
98.23%
83.30%
96.92%
91.93%
98.16%
96.64%
98.90%SCL
SCL-MI
76.8%
79.7%
75.4%
75.4%
66.1%
68.6%
74.0%
75.8%
74.3%
76.2%
75.4%
76.9%
77.5%
75.9%
74.1%
74.1%
83.7%
86.8%
78.7%
78.9%
79.4%
81.4%
84.4%
85.9%
KMM
TCA
5.51
2.37

Wisconsin–Madison, Tech. Rep. 1530, 2006.
[15] K. Nigam, A. K. McCallum, S. Thrun, and T. Mitchell, “Text classification from labeled and unlabeled documents using em,” Machine
Learning, vol. 39, no. 2-3, pp. 103–134, 2000.
[16] A. Blum and T. Mitchell, “Combining labeled and unlabeled data withco-training,” in Proceedings of the Eleventh Annual Conference on
Computational Learning Theory, 1998, pp. 92–100.
[17] T. Joachims, “Transductive inference for text classification using support
vector machines,” in Proceedings of Sixteenth International Conference
on Machine Learning, 1999, pp. 825–830.
[18] X. Zhu and X. Wu, “Class noise handling for effective cost-sensitive
learning by cost-guided iterative classification filtering,” IEEE Transactions on Knowledge and Data Engineering, vol. 18, no. 10, pp. 1435–
1440, 2006.
[19] Q. Yang, C. Ling, X. Chai, and R. Pan, “Test-cost sensitive classification
on data with missing values,” IEEE Transactions on Knowledge andData Engineering, vol. 18, no. 5, pp. 626–638, 2006.
[20] S. Thrun and L. Pratt, Eds., Learning to learn. Norwell, MA, USA:
Kluwer Academic Publishers, 1998.
[21] R. Caruana, “Multitask learning,” Machine Learning, vol. 28(1), pp. 41–
75, 1997.
[22] R. Raina, A. Battle, H. Lee, B. Packer, and A. Y. Ng, “Self-taught
learning: Transfer learning from unlabeled data,” in Proceedings of the24th International Conference on Machine Learning, Corvalis, Oregon,
USA, June 2007, pp. 759–766.
[23] H. DauméIII and D. Marcu, “Domain adaptation for statistical classifiers,” Journal of Artificial Intelligence Research, vol. 26, pp. 101–126,
2006.
[24] B. Zadrozny, “Learning and evaluating classifiers under sample selection
bias,” in Proceedings of the 21st International Conference on Machine
Learning, Banff, Alberta, Canada, July 2004.
[25] H. Shimodaira, “Improving predictive inference under covariate shift by

This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
14

weighting the log-likelihood function,” Journal of Statistical Planningand Inference, vol. 90, pp. 227–244, 2000.
[26] W. Dai, Q. Yang, G. Xue, and Y. Yu, “Self-taught clustering,” inProceedings of the 25th International Conference of Machine Learning.
ACM, July 2008, pp. 200–207.
[27] Z. Wang, Y. Song, and C. Zhang, “Transferred dimensionality reduction,” in Machine Learning and Knowledge Discovery in Databases, European Conference, ECML/PKDD 2008. Antwerp, Belgium: Springer,
September 2008, pp. 550–565.
[28] W. Dai, G. Xue, Q. Yang, and Y. Yu, “Transferring naive bayes classifiers
for text classification,” in Proceedings of the 22rd AAAI Conference on
Artificial Intelligence, Vancouver, British Columbia, Canada, July 2007,
pp. 540–545.
[29] J. Quionero-Candela, M. Sugiyama, A. Schwaighofer, and N. D.
Lawrence, Dataset Shift in Machine Learning. The MIT Press, 2009.
[30] J. Jiang and C. Zhai, “Instance weighting for domain adaptation innlp,” in Proceedings of the 45th Annual Meeting of the Association ofComputational Linguistics. Prague, Czech Republic: Association for
Computational Linguistics, June 2007, pp. 264–271.
[31] X. Liao, Y. Xue, and L. Carin, “Logistic regression with an auxiliary
data source,” in Proceedings of the 21st International Conference on
Machine Learning, Bonn, Germany, August 2005, pp. 505–512.
[32] J. Huang, A. Smola, A. Gretton, K. M. Borgwardt, and B. Schölkopf,“Correcting sample selection bias by unlabeled data,” in Proceedings ofthe 19th Annual Conference on Neural Information Processing Systems,
2007.
[33] S. Bickel, M. Brückner, and T. Scheffer, “Discriminative learning fordiffering training and test distributions,” in Proceedings of the 24thinternational conference on Machine learning. New York, NY, USA:ACM, 2007, pp. 81–88.
[34] M. Sugiyama, S. Nakajima, H. Kashima, P. V. Buenau, and M. Kawanabe, “Direct importance estimation with model selection and its application to covariate shift adaptation,” in Proceedings of the 20th Annual
Conference on Neural Information Processing Systems, Vancouver,
British Columbia, Canada, December 2008.
[35] W. Fan, I. Davidson, B. Zadrozny, and P. S. Yu, “An improved
categorization of classifier’s sensitivity on sample selection bias,” inProceedings of the 5th IEEE International Conference on Data Mining,
2005.
[36] W. Dai, G. Xue, Q. Yang, and Y. Yu, “Co-clustering based classification for out-of-domain documents,” in Proceedings of the 13th ACM
SIGKDD International Conference on Knowledge Discovery and Data
Mining, San Jose, California, USA, August 2007.
[37] R. K. Ando and T. Zhang, “A high-performance semi-supervised learning method for text chunking,” in Proceedings of the 43rd Annual
Meeting on Association for Computational Linguistics. Morristown,
NJ, USA: Association for Computational Linguistics, 2005, pp. 1–9.
[38] J. Blitzer, R. McDonald, and F. Pereira, “Domain adaptation withstructural correspondence learning,” in Proceedings of the Conference on
Empirical Methods in Natural Language, Sydney, Australia, July 2006,
pp. 120–128.
[39] H. Daumé III, “Frustratingly easy domain adaptation,” in Proceedingsof the 45th Annual Meeting of the Association of Computational Linguistics, Prague, Czech Republic, June 2007, pp. 256–263.
[40] A. Argyriou, T. Evgeniou, and M. Pontil, “Multi-task feature learning,”
in Proceedings of the 19th Annual Conference on Neural Information
Processing Systems, Vancouver, British Columbia, Canada, December
2007, pp. 41–48.
[41] A. Argyriou, C. A. Micchelli, M. Pontil, and Y. Ying, “A spectral regularization framework for multi-task structure learning,” in Proceedings ofthe 20th Annual Conference on Neural Information Processing Systems.
Cambridge, MA: MIT Press, 2008, pp. 25–32.
[42] S.-I. Lee, V. Chatalbashev, D. Vickrey, and D. Koller, “Learning ameta-level prior for feature relevance from multiple related tasks,” inProceedings of the 24th International Conference on Machine Learning.
Corvalis, Oregon, USA: ACM, July 2007, pp. 489–496.
[43] T. Jebara, “Multi-task feature and kernel selection for svms,” in Proceedings of the 21st International Conference on Machine Learning. Banff,
Alberta, Canada: ACM, July 2004.
[44] C. Wang and S. Mahadevan, “Manifold alignment using procrustes
analysis,” in Proceedings of the 25th International Conference on
Machine learning. Helsinki, Finland: ACM, July 2008, pp. 1120–1127.
[45] N. D. Lawrence and J. C. Platt, “Learning to learn with the informative
vector machine,” in Proceedings of the 21st International Conference
on Machine Learning. Banff, Alberta, Canada: ACM, July 2004.
[46] E. Bonilla, K. M. Chai, and C. Williams, “Multi-task gaussian process
prediction,” in Proceedings of the 20th Annual Conference on Neural

Information Processing Systems. Cambridge, MA: MIT Press, 2008,
pp. 153–160.
[47] A. Schwaighofer, V. Tresp, and K. Yu, “Learning gaussian process
kernels via hierarchical bayes,” in Proceedings of the 17th Annual
Conference on Neural Information Processing Systems. Cambridge,
MA: MIT Press, 2005, pp. 1209–1216.
[48] T. Evgeniou and M. Pontil, “Regularized multi-task learning,” inProceedings of the 10th ACM SIGKDD International Conference on
Knowledge Discovery and Data Mining. Seattle, Washington, USA:ACM, August 2004, pp. 109–117.
[49] J. Gao, W. Fan, J. Jiang, and J. Han, “Knowledge transfer via multiple
model local structure mapping,” in Proceedings of the 14th ACM
SIGKDD International Conference on Knowledge Discovery and Data
Mining. Las Vegas, Nevada: ACM, August 2008, pp. 283–291.
[50] L. Mihalkova, T. Huynh, and R. J. Mooney, “Mapping and revising
markov logic networks for transfer learning,” in Proceedings of the
22nd AAAI Conference on Artificial Intelligence, Vancouver, British
Columbia, Canada, July 2007, pp. 608–614.
[51] L. Mihalkova and R. J. Mooney, “Transfer learning by mapping withminimal target data,” in Proceedings of the AAAI-2008 Workshop on
Transfer Learning for Complex Tasks, Chicago, Illinois, USA, July 2008.
[52] J. Davis and P. Domingos, “Deep transfer via second-order markov
logic,” in Proceedings of the AAAI-2008 Workshop on Transfer Learning
for Complex Tasks, Chicago, Illinois, USA, July 2008.
[53] P. Wu and T. G. Dietterich, “Improving svm accuracy by training
on auxiliary data sources,” in Proceedings of the 21st International
Conference on Machine Learning. Banff, Alberta, Canada: ACM, July
2004.
[54] U. Rückert and S. Kramer, “Kernel-based inductive transfer,” in Machine
Learning and Knowledge Discovery in Databases, European Conference, ECML/PKDD 2008, ser. Lecture Notes in Computer Science.
Antwerp, Belgium: Springer, September 2008, pp. 220–233.
[55] H. Lee, A. Battle, R. Raina, and A. Y. Ng, “Efficient sparse coding
algorithms,” in Proceedings of the the 19th Annual Conference on Neural
Information Processing Systemss. Cambridge, MA: MIT Press, 2007,
pp. 801–808.
[56] M. Richardson and P. Domingos, “Markov logic networks,” Machine
Learning Journal, vol. 62, no. 1-2, pp. 107–136, 2006.
[57] S. Ramachandran and R. J. Mooney, “Theory refinement of bayesian
networks with hidden variables,” in Proceedings of the 14th International Conference on Machine Learning, Madison, Wisconson, USA,
July 1998, pp. 454–462.
[58] A. Arnold, R. Nallapati, and W. W. Cohen, “A comparative study ofmethods for transductive transfer learning,” in Proceedings of the 7thIEEE International Conference on Data Mining Workshops. Washington, DC, USA: IEEE Computer Society, 2007, pp. 77–82.
[59] T. Joachims, “Transductive inference for text classification using support
vector machines,” in Proceedings of the Sixteenth International Conference on Machine Learning, San Francisco, CA, USA, 1999, pp. 200–
209.
[60] V. N. Vapnik, Statistical Learning Theory.
New York: WileyInterscience, September 1998.
[61] S. Ben-David, J. Blitzer, K. Crammer, and F. Pereira, “Analysis of representations for domain adaptation,” in Proceedings of the 20th Annual
Conference on Neural Information Processing Systems. Cambridge,
MA: MIT Press, 2007, pp. 137–144.
[62] J. Blitzer, K. Crammer, A. Kulesza, F. Pereira, and J. Wortman, “Learning bounds for domain adaptation,” in Proceedings of the 21st Annual
Conference on Neural Information Processing Systems. Cambridge,
MA: MIT Press, 2008, pp. 129–136.
[63] D. Xing, W. Dai, G.-R. Xue, and Y. Yu, “Bridged refinement for transfer
learning,” in 11th European Conference on Principles and Practice ofKnowledge Discovery in Databases, ser. Lecture Notes in Computer
Science. Warsaw, Poland: Springer, September 2007, pp. 324–335.
[64] X. Ling, W. Dai, G.-R. Xue, Q. Yang, and Y. Yu, “Spectral domaintransfer learning,” in Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Las
Vegas, Nevada: ACM, August 2008, pp. 488–496.
[65] G.-R. Xue, W. Dai, Q. Yang, and Y. Yu, “Topic-bridged plsa forcross-domain text classification,” in Proceedings of the 31st Annual
International ACM SIGIR Conference on Research and Development
in Information Retrieval. Singapore: ACM, July 2008, pp. 627–634.
[66] S. J. Pan, J. T. Kwok, and Q. Yang, “Transfer learning via dimensionality
reduction,” in Proceedings of the 23rd AAAI Conference on Artificial
Intelligence, Chicago, Illinois, USA, July 2008, pp. 677–682.

This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
15[67] S. J. Pan, I. W. Tsang, J. T. Kwok, and Q. Yang, “Domain adaptation via
transfer component analysis,” in Proceedings of the 21st International
Joint Conference on Artificial Intelligence, Pasadena, California, 2009.
[68] M. M. H. Mahmud and S. R. Ray, “Transfer learning using kolmogorov
complexity: Basic theory and empirical evaluations,” in Proceedings ofthe 20th Annual Conference on Neural Information Processing Systems.
Cambridge, MA: MIT Press, 2008, pp. 985–992.
[69] E. Eaton, M. desJardins, and T. Lane, “Modeling transfer relationships
between learning tasks for improved inductive transfer,” in Machine
Learning and Knowledge Discovery in Databases, European Conference, ECML/PKDD 2008, ser. Lecture Notes in Computer Science.
Antwerp, Belgium: Springer, September 2008, pp. 317–332.
[70] M. T. Rosenstein, Z. Marx, and L. P. Kaelbling, “To transfer or not totransfer,” in a NIPS-05 Workshop on Inductive Transfer: 10 Years Later,
December 2005.
[71] S. Ben-David and R. Schuller, “Exploiting task relatedness for multiple
task learning,” in Proceedings of the Sixteenth Annual Conference on
Learning Theory. San Francisco: Morgan Kaufmann, 2003, pp. 825–
830.
[72] B. Bakker and T. Heskes, “Task clustering and gating for bayesian
multitask learning,” Journal of Machine Learning Reserch, vol. 4, pp.
83–99, 2003.
[73] A. Argyriou, A. Maurer, and M. Pontil, “An algorithm for transfer learning in a heterogeneous environment,” in Machine Learning and Knowledge Discovery in Databases, European Conference, ECML/PKDD
2008, ser. Lecture Notes in Computer Science. Antwerp, Belgium:
Springer, September 2008, pp. 71–85.
[74] R. Raina, A. Y. Ng, and D. Koller, “Constructing informative priors usingtransfer learning.” in Proceedings of the 23rd International Conference
on Machine Learning, Pittsburgh, Pennsylvania, USA, June 2006, pp.
713–720.
[75] J. Yin, Q. Yang, and L. M. Ni, “Adaptive temporal radio maps for indoor
location estimation,” in Proceedings of the 3rd IEEE International
Conference on Pervasive Computing and Communications, Kauai Island,
Hawaii, USA, March 2005.
[76] S. J. Pan, J. T. Kwok, Q. Yang, and J. J. Pan, “Adaptive localization ina dynamic WiFi environment through multi-view learning,” in Proceedings of the 22nd AAAI Conference on Artificial Intelligence, Vancouver,
British Columbia, Canada, July 2007, pp. 1108–1113.
[77] V. W. Zheng, Q. Yang, W. Xiang, and D. Shen, “Transferring localization
models over time,” in Proceedings of the 23rd AAAI Conference on
Artificial Intelligence, Chicago, Illinois, USA, July 2008, pp. 1421–1426.
[78] S. J. Pan, D. Shen, Q. Yang, and J. T. Kwok, “Transferring localization
models across space,” in Proceedings of the 23rd AAAI Conference on
Artificial Intelligence, Chicago, Illinois, USA, July 2008, pp. 1383–1388.
[79] V. W. Zheng, S. J. Pan, Q. Yang, and J. J. Pan, “Transferring multi-device
localization models using latent multi-task learning,” in Proceedings ofthe 23rd AAAI Conference on Artificial Intelligence, Chicago, Illinois,
USA, July 2008, pp. 1427–1432.
[80] H. Zhuo, Q. Yang, D. H. Hu, and L. Li, “Transferring knowledge
from another domain for learning action models,” in Proceedings of
10th Pacific Rim International Conference on Artificial Intelligence,
December 2008.
[81] V. C. Raykar, B. Krishnapuram, J. Bi, M. Dundar, and R. B. Rao,
“Bayesian multiple instance learning: automatic feature selection andinductive transfer,” in Proceedings of the 25th International Conference
on Machine learning. Helsinki, Finland: ACM, July 2008, pp. 808–815.
[82] X. Ling, G.-R. Xue, W. Dai, Y. Jiang, Q. Yang, and Y. Yu, “Can chinese
web pages be classified with english data source?” in Proceedings ofthe 17th International Conference on World Wide Web. Beijing, China:
ACM, April 2008, pp. 969–978.
[83] Q. Yang, S. J. Pan, and V. W. Zheng, “Estimating location using Wi-Fi,”IEEE Intelligent Systems, vol. 23, no. 1, pp. 8–13, 2008.
[84] X. Shi, W. Fan, and J. Ren, “Actively transfer domain knowledge,” inMachine Learning and Knowledge Discovery in Databases, European
Conference, ECML/PKDD 2008, ser. Lecture Notes in Computer Science. Antwerp, Belgium: Springer, September 2008, pp. 342–357.
[85] G. Kuhlmann and P. Stone, “Graph-based domain mapping for transfer
learning in general games,” in 18th European Conference on Machine
Learning, ser. Lecture Notes in Computer Science. Warsaw, Poland:
Springer, September 2007, pp. 188–200.
[86] W. Dai, Y. Chen, G.-R. Xue, Q. Yang, and Y. Yu, “Translated learning,”
in Proceedings of 21st Annual Conference on Neural Information
Processing Systems, 2008.
[87] B. Li, Q. Yang, and X. Xue, “Transfer learning for collaborative
filtering via a rating-matrix generative model,” in Proceedings of the

26th International Conference on Machine Learning, Montreal, Quebec,
Canada, June 2009.
[88] B. Li, Q. Yang, and X. Xue, “Can movies and books collaborate? - crossdomain collaborative filtering for sparsity reduction,” in Proceedingsof the 21st International Joint Conference on Artificial Intelligence,
Pasadena, California, USA, July 2009.

Sinno Jialin Pan is a PhD candidate in the
Department of Computer Science and Engineering, the Hong Kong University of Science andTechnology. He received the MS and BS degrees from Applied Mathematics Department,
Sun Yat-sen University, China, in 2003 and2005, respectively. His research interests include transfer learning, semi-supervised learning, and their applications in pervasive computing and Web mining. He is a member of AAAI.
Contact him at the Department of Computer
Science and Engineering, Hong Kong Univ. of Science and Technology, Clearwater Bay, Kowloon, Hong Kong; sinnopan@cse.ust.hk;
http://www.cse.ust.hk/∼sinnopan.

Qiang Yang is a faculty member in the Hong
Kong University of Science and Technology’s
Department of Computer Science and Engineering. His research interests are data mining andmachine learning, AI planning and sensor based
activity recognition. He received his PhD degree in Computer Science from the University of
Maryland, College Park, and Bachelor’s degreefrom Peking University in Astrophysics. He is afellow of IEEE, member of AAAI and ACM, aformer associate editor for the IEEE TKDE, anda current associate editor for IEEE Intelligent Systems. Contact him
at the Department of Computer Science and Engineering, Hong Kong
Univ. of Science and Technology, Clearwater Bay, Kowloon, Hong Kong;
qyang@cse.ust.hk; http://www.cse.ust.hk/∼qyang.