pwc-archive/papers-with-abstracts huggingface dataset:
A Comprehensive Evaluation of Machine Learning Techniques for Cancer Class Prediction Based on Microarray Data
Authors: Khalid Raza, Atif N Hasan
Date: 2013-07-26 00:00:00
Tasks: Attribute, BIG-bench Machine Learning, Specificity

Prostate cancer is among the most common cancer in males and its
heterogeneity is well known. Its early detection helps making therapeutic
decision. There is no standard technique or procedure yet which is full-proof
in predicting cancer class. The genomic level changes can be detected in gene
expression data and those changes may serve as standard model for any random
cancer data for class prediction. Various techniques were implied on prostate
cancer data set in order to accurately predict cancer class including machine
learning techniques. Huge number of attributes and few number of sample in
microarray data leads to poor machine learning, therefore the most challenging
part is attribute reduction or non significant gene reduction. In this work we
have compared several machine learning techniques for their accuracy in
predicting the cancer class. Machine learning is effective when number of
attributes (genes) are larger than the number of samples which is rarely
possible with gene expression data. Attribute reduction or gene filtering is
absolutely required in order to make the data more meaningful as most of the
genes do not participate in tumor development and are irrelevant for cancer
prediction. Here we have applied combination of statistical techniques such as
inter-quartile range and t-test, which has been effective in filtering
significant genes and minimizing noise from data. Further we have done a
comprehensive evaluation of ten state-of-the-art machine learning techniques
for their accuracy in class prediction of prostate cancer. Out of these
techniques, Bayes Network out performed with an accuracy of 94.11% followed by
Navie Bayes with an accuracy of 91.17%. To cross validate our results, we
modified our training dataset in six different way and found that average
sensitivity, specificity, precision and accuracy of Bayes Network is highest
among all other techniques used.
Infinite Mixtures of Multivariate Gaussian Processes
Authors: Shiliang Sun
Date: 2013-07-26 00:00:00
Tasks: Gaussian Processes, regression

This paper presents a new model called infinite mixtures of multivariate
Gaussian processes, which can be used to learn vector-valued functions and
applied to multitask learning. As an extension of the single multivariate
Gaussian process, the mixture model has the advantages of modeling multimodal
data and alleviating the computationally cubic complexity of the multivariate
Gaussian process. A Dirichlet process prior is adopted to allow the (possibly
infinite) number of mixture components to be automatically inferred from
training data, and Markov chain Monte Carlo sampling techniques are used for
parameter and latent variable inference. Preliminary experimental results on
multivariate regression show the feasibility of the proposed model.
Multi-view Laplacian Support Vector Machines
Authors: Shiliang Sun
Date: 2013-07-26 00:00:00

We propose a new approach, multi-view Laplacian support vector machines
(SVMs), for semi-supervised learning under the multi-view scenario. It
integrates manifold regularization and multi-view regularization into the usual
formulation of SVMs and is a natural extension of SVMs from supervised learning
to multi-view semi-supervised learning. The function optimization problem in a
reproducing kernel Hilbert space is converted to an optimization in a
finite-dimensional Euclidean space. After providing a theoretical bound for the
generalization performance of the proposed method, we further give a
formulation of the empirical Rademacher complexity which affects the bound
significantly. From this bound and the empirical Rademacher complexity, we can
gain insights into the roles played by different regularization terms to the
generalization performance. Experimental results on synthetic and real-world
data sets are presented, which validate the effectiveness of the proposed
multi-view Laplacian SVMs approach.
Simulating Ability: Representing Skills in Games
Authors: Magnus Lie Hetland
Date: 2013-06-30 00:00:00

Throughout the history of games, representing the abilities of the various
agents acting on behalf of the players has been a central concern. With
increasingly sophisticated games emerging, these simulations have become more
realistic, but the underlying mechanisms are still, to a large extent, of an ad
hoc nature. This paper proposes using a logistic model from psychometrics as a
unified mechanism for task resolution in simulation-oriented games.
A Novel Architecture For Question Classification Based Indexing Scheme For Efficient Question Answering
Authors: Renu Mudgal, Rosy Madaan, A. K. Sharma, Ashutosh Dixit
Date: 2013-07-26 00:00:00
Tasks: Classification, General Classification, Information Retrieval, Question Answering, Retrieval

Question answering system can be seen as the next step in information
retrieval, allowing users to pose question in natural language and receive
compact answers. For the Question answering system to be successful, research
has shown that the correct classification of question with respect to the
expected answer type is requisite. We propose a novel architecture for question
classification and searching in the index, maintained on the basis of expected
answer types, for efficient question answering. The system uses the criteria
for Answer Relevance Score for finding the relevance of each answer returned by
the system. On analysis of the proposed system, it has been found that the
system has shown promising results than the existing systems based on question
classification.
An Efficient Primal-Dual Prox Method for Non-Smooth Optimization
Authors: Tianbao Yang, Mehrdad Mahdavi, Rong Jin, Shenghuo Zhu
Date: 2012-01-24 00:00:00
Tasks: BIG-bench Machine Learning

We study the non-smooth optimization problems in machine learning, where both
the loss function and the regularizer are non-smooth functions. Previous
studies on efficient empirical loss minimization assume either a smooth loss
function or a strongly convex regularizer, making them unsuitable for
non-smooth optimization. We develop a simple yet efficient method for a family
of non-smooth optimization problems where the dual form of the loss function is
bilinear in primal and dual variables. We cast a non-smooth optimization
problem into a minimax optimization problem, and develop a primal dual prox
method that solves the minimax optimization problem at a rate of $O(1/T)$
{assuming that the proximal step can be efficiently solved}, significantly
faster than a standard subgradient descent method that has an $O(1/\sqrt{T})$
convergence rate. Our empirical study verifies the efficiency of the proposed
method for various non-smooth optimization problems that arise ubiquitously in
machine learning by comparing it to the state-of-the-art first order methods.
Memcapacitive neural networks
Authors: Y. V. Pershin, M. Di Ventra
Date: 2013-07-26 00:00:00

We show that memcapacitive (memory capacitive) systems can be used as
synapses in artificial neural networks. As an example of our approach, we
discuss the architecture of an integrate-and-fire neural network based on
memcapacitive synapses. Moreover, we demonstrate that the
spike-timing-dependent plasticity can be simply realized with some of these
devices. Memcapacitive synapses are a low-energy alternative to memristive
synapses for neuromorphic computation.
Sequential Transfer in Multi-armed Bandit with Finite Set of Models
Authors: Mohammad Gheshlaghi Azar, Alessandro Lazaric, Emma Brunskill
Date: 2013-07-25 00:00:00
Proceeding: NeurIPS 2013 12
Tasks: Lifelong learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Learning from prior tasks and transferring that experience to improve future
performance is critical for building lifelong learning agents. Although results
in supervised and reinforcement learning show that transfer may significantly
improve the learning performance, most of the literature on transfer is focused
on batch learning tasks. In this paper we study the problem of
\textit{sequential transfer in online learning}, notably in the multi-armed
bandit framework, where the objective is to minimize the cumulative regret over
a sequence of tasks by incrementally transferring knowledge from prior tasks.
We introduce a novel bandit algorithm based on a method-of-moments approach for
the estimation of the possible tasks and derive regret bounds for it.
A Propound Method for the Improvement of Cluster Quality
Authors: Shveta Kundra Bhatia, V. S. Dixit
Date: 2013-07-25 00:00:00
Tasks: Clustering

In this paper Knockout Refinement Algorithm (KRA) is proposed to refine
original clusters obtained by applying SOM and K-Means clustering algorithms.
KRA Algorithm is based on Contingency Table concepts. Metrics are computed for
the Original and Refined Clusters. Quality of Original and Refined Clusters are
compared in terms of metrics. The proposed algorithm (KRA) is tested in the
educational domain and results show that it generates better quality clusters
in terms of improved metric values.
Information content versus word length in natural language: A reply to Ferrer-i-Cancho and Moscoso del Prado Martin [arXiv:1209.1751]
Authors: Steven T. Piantadosi, Harry Tily, Edward Gibson
Date: 2013-07-25 00:00:00

Recently, Ferrer i Cancho and Moscoso del Prado Martin [arXiv:1209.1751]
argued that an observed linear relationship between word length and average
surprisal (Piantadosi, Tily, & Gibson, 2011) is not evidence for communicative
efficiency in human language. We discuss several shortcomings of their approach
and critique: their model critically rests on inaccurate assumptions, is
incapable of explaining key surprisal patterns in language, and is incompatible
with recent behavioral results. More generally, we argue that statistical
models must not critically rely on assumptions that are incompatible with the
real system under study.
Does generalization performance of $l^q$ regularization learning depend on $q$? A negative example
Authors: Shaobo Lin, Chen Xu, Jingshan Zeng, Jian Fang
Date: 2013-07-25 00:00:00

$l^q$-regularization has been demonstrated to be an attractive technique in machine learning and statistical modeling. It attempts to improve the generalization (prediction) capability of a machine (model) through appropriately shrinking its coefficients. The shape of a $l^q$ estimator differs in varying choices of the regularization order $q$. In particular, $l^1$ leads to the LASSO estimate, while $l^{2}$ corresponds to the smooth ridge regression. This makes the order $q$ a potential tuning parameter in applications. To facilitate the use of $l^{q}$-regularization, we intend to seek for a modeling strategy where an elaborative selection on $q$ is avoidable. In this spirit, we place our investigation within a general framework of $l^{q}$-regularized kernel learning under a sample dependent hypothesis space (SDHS). For a designated class of kernel functions, we show that all $l^{q}$ estimators for $0< q < \infty$ attain similar generalization error bounds. These estimated bounds are almost optimal in the sense that up to a logarithmic factor, the upper and lower bounds are asymptotically identical. This finding tentatively reveals that, in some modeling contexts, the choice of $q$ might not have a strong impact in terms of the generalization capability. From this perspective, $q$ can be arbitrarily specified, or specified merely by other no generalization criteria like smoothness, computational complexity, sparsity, etc..
Scaling the Indian Buffet Process via Submodular Maximization
Authors: Colorado Reed, Zoubin Ghahramani
Date: 2013-04-11 00:00:00

Inference for latent feature models is inherently difficult as the inference
space grows exponentially with the size of the input data and number of latent
features. In this work, we use Kurihara & Welling (2008)'s
maximization-expectation framework to perform approximate MAP inference for
linear-Gaussian latent feature models with an Indian Buffet Process (IBP)
prior. This formulation yields a submodular function of the features that
corresponds to a lower bound on the model evidence. By adding a constant to
this function, we obtain a nonnegative submodular function that can be
maximized via a greedy algorithm that obtains at least a one-third
approximation to the optimal solution. Our inference method scales linearly
with the size of the input data, and we show the efficacy of our method on the
largest datasets currently analyzed using an IBP model.
When is the majority-vote classifier beneficial?
Authors: Mu Zhu
Date: 2013-07-24 00:00:00
Tasks: Binary Classification, General Classification

In his seminal work, Schapire (1990) proved that weak classifiers could be
improved to achieve arbitrarily high accuracy, but he never implied that a
simple majority-vote mechanism could always do the trick. By comparing the
asymptotic misclassification error of the majority-vote classifier with the
average individual error, we discover an interesting phase-transition
phenomenon. For binary classification with equal prior probabilities, our
result implies that, for the majority-vote mechanism to work, the collection of
weak classifiers must meet the minimum requirement of having an average true
positive rate of at least 50% and an average false positive rate of at most
50%.
Cluster Trees on Manifolds
Authors: Sivaraman Balakrishnan, Srivatsan Narayanan, Alessandro Rinaldo, Aarti Singh, Larry Wasserman
Date: 2013-07-24 00:00:00
Proceeding: NeurIPS 2013 12
Tasks: Clustering

In this paper we investigate the problem of estimating the cluster tree for a
density $f$ supported on or near a smooth $d$-dimensional manifold $M$
isometrically embedded in $\mathbb{R}^D$. We analyze a modified version of a
$k$-nearest neighbor based algorithm recently proposed by Chaudhuri and
Dasgupta. The main results of this paper show that under mild assumptions on
$f$ and $M$, we obtain rates of convergence that depend on $d$ only but not on
the ambient dimension $D$. We also show that similar (albeit non-algorithmic)
results can be obtained for kernel density estimators. We sketch a construction
of a sample complexity lower bound instance for a natural class of manifold
oblivious clustering algorithms. We further briefly consider the known manifold
case and show that in this case a spatially adaptive algorithm achieves better
rates.
Human and Automatic Evaluation of English-Hindi Machine Translation
Authors: Nisheeth Joshi, Hemant Darbari, Iti Mathur
Date: 2013-07-23 00:00:00
Tasks: Machine Translation, Sentence, Translation

For the past 60 years, Research in machine translation is going on. For the
development in this field, a lot of new techniques are being developed each
day. As a result, we have witnessed development of many automatic machine
translators. A manager of machine translation development project needs to know
the performance increase/decrease, after changes have been done in his system.
Due to this reason, a need for evaluation of machine translation systems was
felt. In this article, we shall present the evaluation of some machine
translators. This evaluation will be done by a human evaluator and by some
automatic evaluation metrics, which will be done at sentence, document and
system level. In the end we shall also discuss the comparison between the
evaluations.
Generative, Fully Bayesian, Gaussian, Openset Pattern Classifier
Authors: Niko Brummer
Date: 2013-07-23 00:00:00
Tasks: Form

This report works out the details of a closed-form, fully Bayesian,
multiclass, openset, generative pattern classifier using multivariate Gaussian
likelihoods, with conjugate priors. The generative model has a common
within-class covariance, which is proportional to the between-class covariance
in the conjugate prior. The scalar proportionality constant is the only plugin
parameter. All other model parameters are intergated out in closed form. An
expression is given for the model evidence, which can be used to make plugin
estimates for the proportionality constant. Pattern recognition is done via the
predictive likeihoods of classes for which training data is available, as well
as a predicitve likelihood for any as yet unseen class.
Storing non-uniformly distributed messages in networks of neural cliques
Authors: Bartosz Boguslawski, Vincent Gripon, Fabrice Seguin, Frédéric Heitzmann
Date: 2013-07-24 00:00:00
Tasks: Retrieval

Associative memories are data structures that allow retrieval of stored
messages from part of their content. They thus behave similarly to human brain
that is capable for instance of retrieving the end of a song given its
beginning. Among different families of associative memories, sparse ones are
known to provide the best efficiency (ratio of the number of bits stored to
that of bits used). Nevertheless, it is well known that non-uniformity of the
stored messages can lead to dramatic decrease in performance. We introduce
several strategies to allow efficient storage of non-uniform messages in
recently introduced sparse associative memories. We analyse and discuss the
methods introduced. We also present a practical application example.
Matching-Constrained Active Contours
Authors: Junyan Wang, Kap Luk Chan
Date: 2013-07-24 00:00:00
Tasks: Object, Segmentation, Semantic Segmentation

In object segmentation by active contours, the initial contour is often
required. Conventionally, the initial contour is provided by the user. This
paper extends the conventional active contour model by incorporating feature
matching in the formulation, which gives rise to a novel matching-constrained
active contour. The numerical solution to the new optimization model provides
an automated framework of object segmentation without user intervention. The
main idea is to incorporate feature point matching as a constraint in active
contour models. To this effect, we obtain a mathematical model of interior
points to boundary contour such that matching of interior feature points gives
contour alignment, and we formulate the matching score as a constraint to
active contour model such that the feature matching of maximum score that gives
the contour alignment provides the initial feasible solution to the constrained
optimization model of segmentation. The constraint also ensures that the
optimal contour does not deviate too much from the initial contour.
Projected-gradient descent equations are derived to solve the constrained
optimization. In the experiments, we show that our method is capable of
achieving the automatic object segmentation, and it outperforms the related
methods.
Multi-View Learning for Web Spam Detection
Authors: Ali Hadian, Behrouz Minaei-Bidgoli
Date: 2013-05-16 00:00:00
Tasks: Classification, General Classification, MULTI-VIEW LEARNING, Spam detection

Spam pages are designed to maliciously appear among the top search results by
excessive usage of popular terms. Therefore, spam pages should be removed using
an effective and efficient spam detection system. Previous methods for web spam
classification used several features from various information sources (page
contents, web graph, access logs, etc.) to detect web spam. In this paper, we
follow page-level classification approach to build fast and scalable spam
filters. We show that each web page can be classified with satisfiable accuracy
using only its own HTML content. In order to design a multi-view classification
system, we used state-of-the-art spam classification methods with distinct
feature sets (views) as the base classifiers. Then, a fusion model is learned
to combine the output of the base classifiers and make final prediction.
Results show that multi-view learning significantly improves the classification
performance, namely AUC by 22%, while providing linear speedup for parallel
execution.
A novel approach of solving the CNF-SAT problem
Authors: Xili Wang
Date: 2013-07-24 00:00:00

In this paper, we discussed CNF-SAT problem (NP-Complete problem) and
analysis two solutions that can solve the problem, the PL-Resolution algorithm
and the WalkSAT algorithm. PL-Resolution is a sound and complete algorithm that
can be used to determine satisfiability and unsatisfiability with certainty.
WalkSAT can determine satisfiability if it finds a model, but it cannot
guarantee to find a model even there exists one. However, WalkSAT is much
faster than PL-Resolution, which makes WalkSAT more practical; and we have
analysis the performance between these two algorithms, and the performance of
WalkSAT is acceptable if the problem is not so hard.
Good Debt or Bad Debt: Detecting Semantic Orientations in Economic Texts
Authors: Pekka Malo, Ankur Sinha, Pyry Takala, Pekka Korhonen, Jyrki Wallenius
Date: 2013-07-19 00:00:00
Tasks: Sentence, Sentiment Analysis

The use of robo-readers to analyze news texts is an emerging technology trend
in computational finance. In recent research, a substantial effort has been
invested to develop sophisticated financial polarity-lexicons that can be used
to investigate how financial sentiments relate to future company performance.
However, based on experience from other fields, where sentiment analysis is
commonly applied, it is well-known that the overall semantic orientation of a
sentence may differ from the prior polarity of individual words. The objective
of this article is to investigate how semantic orientations can be better
detected in financial and economic news by accommodating the overall
phrase-structure information and domain-specific use of language. Our three
main contributions are: (1) establishment of a human-annotated finance
phrase-bank, which can be used as benchmark for training and evaluating
alternative models; (2) presentation of a technique to enhance financial
lexicons with attributes that help to identify expected direction of events
that affect overall sentiment; (3) development of a linearized phrase-structure
model for detecting contextual semantic orientations in financial and economic
news texts. The relevance of the newly added lexicon features and the benefit
of using the proposed learning-algorithm are demonstrated in a comparative
study against previously used general sentiment models as well as the popular
word frequency models used in recent financial studies. The proposed framework
is parsimonious and avoids the explosion in feature-space caused by the use of
conventional n-gram features.