pwc-archive/papers-with-abstracts huggingface dataset:
2D View Aggregation for Lymph Node Detection Using a Shallow Hierarchy of Linear Classifiers
Authors: Ari Seff, Le Lu, Kevin M. Cherry, Holger Roth, Jiamin Liu, Shijun Wang, Joanne Hoffman, Evrim B. Turkbey, Ronald M. Summers
Date: 2014-08-14 00:00:00
Tasks: 2D Object Detection, object-detection, Object Detection, Sensitivity

Enlarged lymph nodes (LNs) can provide important information for cancer
diagnosis, staging, and measuring treatment reactions, making automated
detection a highly sought goal. In this paper, we propose a new algorithm
representation of decomposing the LN detection problem into a set of 2D object
detection subtasks on sampled CT slices, largely alleviating the curse of
dimensionality issue. Our 2D detection can be effectively formulated as linear
classification on a single image feature type of Histogram of Oriented
Gradients (HOG), covering a moderate field-of-view of 45 by 45 voxels. We
exploit both simple pooling and sparse linear fusion schemes to aggregate these
2D detection scores for the final 3D LN detection. In this manner, detection is
more tractable and does not need to perform perfectly at instance level (as
weak hypotheses) since our aggregation process will robustly harness collective
information for LN detection. Two datasets (90 patients with 389 mediastinal
LNs and 86 patients with 595 abdominal LNs) are used for validation.
Cross-validation demonstrates 78.0% sensitivity at 6 false positives/volume
(FP/vol.) (86.1% at 10 FP/vol.) and 73.1% sensitivity at 6 FP/vol. (87.2% at 10
FP/vol.), for the mediastinal and abdominal datasets respectively. Our results
compare favorably to previous state-of-the-art methods.
Exact and empirical estimation of misclassification probability
Authors: Victor Nedelko
Date: 2014-08-14 00:00:00
Tasks: General Classification

We discuss the problem of risk estimation in the classification problem, with
specific focus on finding distributions that maximize the confidence intervals
of risk estimation. We derived simple analytic approximations for the maximum
bias of empirical risk for histogram classifier. We carry out a detailed study
on using these analytic estimates for empirical estimation of risk.
Analysis of Gait Pattern to Recognize the Human Activities
Authors: Jay Prakash Gupta, Pushkar Dixit, Nishant Singh, Vijay Bhaskar Semwal
Date: 2014-07-18 00:00:00
Tasks: Activity Recognition, Human Activity Recognition, Retrieval, Video Retrieval

Human activity recognition based on the computer vision is the process of
labelling image sequences with action labels. Accurate systems for this problem
are applied in areas such as visual surveillance, human computer interaction
and video retrieval.
Recognition of Handwritten Persian/Arabic Numerals Based on Robust Feature Set and K-NN Classifier
Authors: Reza Azad, Fatemeh Davami, Hamid Reza Shayegh
Date: 2014-07-24 00:00:00

This paper has been withdrawn by the author due to a crucial sign error in
equation 2 and some mistake in Table 1 information. please let me for changing
this information and updating this paper.
Addendum on the scoring of Gaussian directed acyclic graphical models
Authors: Jack Kuipers, Giusi Moffa, David Heckerman
Date: 2014-02-27 00:00:00

We provide a correction to the expression for scoring Gaussian directed acyclic graphical models derived in Geiger and Heckerman [Ann. Statist. 30 (2002) 1414-1440] and discuss how to evaluate the score efficiently.
Toward Automated Discovery of Artistic Influence
Authors: Babak Saleh, Kanako Abe, Ravneet Singh Arora, Ahmed Elgammal
Date: 2014-08-14 00:00:00
Tasks: General Classification

Considering the huge amount of art pieces that exist, there is valuable
information to be discovered. Examining a painting, an expert can determine its
style, genre, and the time period that the painting belongs. One important task
for art historians is to find influences and connections between artists. Is
influence a task that a computer can measure? The contribution of this paper is
in exploring the problem of computer-automated suggestion of influences between
artists, a problem that was not addressed before in a general setting. We first
present a comparative study of different classification methodologies for the
task of fine-art style classification. A two-level comparative study is
performed for this classification problem. The first level reviews the
performance of discriminative vs. generative models, while the second level
touches the features aspect of the paintings and compares semantic-level
features vs. low-level and intermediate-level features present in the painting.
Then, we investigate the question "Who influenced this artist?" by looking at
his masterpieces and comparing them to others. We pose this interesting
question as a knowledge discovery problem. For this purpose, we investigated
several painting-similarity and artist-similarity measures. As a result, we
provide a visualization of artists (Map of Artists) based on the similarity
between their works
Cortical Processing with Thermodynamic-RAM
Authors: M. Alexander Nugent, Timothy W. Molter
Date: 2014-08-14 00:00:00
Tasks: BIG-bench Machine Learning, Clustering, Combinatorial Optimization

AHaH computing forms a theoretical framework from which a
biologically-inspired type of computing architecture can be built where, unlike
von Neumann systems, memory and processor are physically combined. In this
paper we report on an incremental step beyond the theoretical framework of AHaH
computing toward the development of a memristor-based physical neural
processing unit (NPU), which we call Thermodynamic-RAM (kT-RAM). While the
power consumption and speed dominance of such an NPU over von Neumann
architectures for machine learning applications is well appreciated,
Thermodynamic-RAM offers several advantages over other hardware approaches to
adaptation and learning. Benefits include general-purpose use, a simple yet
flexible instruction set and easy integration into existing digital platforms.
We present a high level design of kT-RAM and a formal definition of its
instruction set. We report the completion of a kT-RAM emulator and the
successful port of all previous machine learning benchmark applications
including unsupervised clustering, supervised and unsupervised classification,
complex signal prediction, unsupervised robotic actuation and combinatorial
optimization. Lastly, we extend a previous MNIST hand written digits benchmark
application, to show that an extra step of reading the synaptic states of AHaH
nodes during the train phase (healing) alone results in plasticity that
improves the classifier's performance, bumping our best F1 score up to 99.5%.
Indefinitely Oscillating Martingales
Authors: Jan Leike, Marcus Hutter
Date: 2014-08-14 00:00:00

We construct a class of nonnegative martingale processes that oscillate
indefinitely with high probability. For these processes, we state a uniform
rate of the number of oscillations and show that this rate is asymptotically
close to the theoretical upper bound. These bounds on probability and
expectation of the number of upcrossings are compared to classical bounds from
the martingale literature. We discuss two applications. First, our results
imply that the limit of the minimum description length operator may not exist.
Second, we give bounds on how often one can change one's belief in a given
hypothesis when observing a stream of data.
Convergence rate of Bayesian tensor estimator: Optimal rate without restricted strong convexity
Authors: Taiji Suzuki
Date: 2014-08-13 00:00:00
Tasks: Collaborative Filtering, Multi-Task Learning, regression

In this paper, we investigate the statistical convergence rate of a Bayesian
low-rank tensor estimator. Our problem setting is the regression problem where
a tensor structure underlying the data is estimated. This problem setting
occurs in many practical applications, such as collaborative filtering,
multi-task learning, and spatio-temporal data analysis. The convergence rate is
analyzed in terms of both in-sample and out-of-sample predictive accuracies. It
is shown that a near optimal rate is achieved without any strong convexity of
the observation. Moreover, we show that the method has adaptivity to the
unknown rank of the true tensor, that is, the near optimal rate depending on
the true rank is achieved even if it is not known a priori.
Fastfood: Approximate Kernel Expansions in Loglinear Time
Authors: Quoc Viet Le, Tamas Sarlos, Alexander Johannes Smola
Date: 2014-08-13 00:00:00

Despite their successes, what makes kernel methods difficult to use in many
large scale problems is the fact that storing and computing the decision
function is typically expensive, especially at prediction time. In this paper,
we overcome this difficulty by proposing Fastfood, an approximation that
accelerates such computation significantly. Key to Fastfood is the observation
that Hadamard matrices, when combined with diagonal Gaussian matrices, exhibit
properties similar to dense Gaussian random matrices. Yet unlike the latter,
Hadamard and diagonal matrices are inexpensive to multiply and store. These two
matrices can be used in lieu of Gaussian matrices in Random Kitchen Sinks
proposed by Rahimi and Recht (2009) and thereby speeding up the computation for
a large range of kernel functions. Specifically, Fastfood requires O(n log d)
time and O(n) storage to compute n non-linear basis functions in d dimensions,
a significant improvement from O(nd) computation and storage, without
sacrificing accuracy.
  Our method applies to any translation invariant and any dot-product kernel,
such as the popular RBF kernels and polynomial kernels. We prove that the
approximation is unbiased and has low variance. Experiments show that we
achieve similar accuracy to full kernel expansions and Random Kitchen Sinks
while being 100x faster and using 1000x less memory. These improvements,
especially in terms of memory usage, make kernel methods more practical for
applications that have large training sets and/or require real-time prediction.
Learning a hyperplane classifier by minimizing an exact bound on the VC dimension
Authors: Jayadeva
Date: 2014-08-12 00:00:00

The VC dimension measures the capacity of a learning machine, and a low VC
dimension leads to good generalization. While SVMs produce state-of-the-art
learning performance, it is well known that the VC dimension of a SVM can be
unbounded; despite good results in practice, there is no guarantee of good
generalization. In this paper, we show how to learn a hyperplane classifier by
minimizing an exact, or \boldmath{$\Theta$} bound on its VC dimension. The
proposed approach, termed as the Minimal Complexity Machine (MCM), involves
solving a simple linear programming problem. Experimental results show, that on
a number of benchmark datasets, the proposed approach learns classifiers with
error rates much less than conventional SVMs, while often using fewer support
vectors. On many benchmark datasets, the number of support vectors is less than
one-tenth the number used by SVMs, indicating that the MCM does indeed learn
simpler representations.
Marginal Likelihoods for Distributed Parameter Estimation of Gaussian Graphical Models
Authors: Zhaoshi Meng, Dennis Wei, Ami Wiesel, Alfred O. Hero III
Date: 2013-03-19 00:00:00
Tasks: parameter estimation

We consider distributed estimation of the inverse covariance matrix, also
called the concentration or precision matrix, in Gaussian graphical models.
Traditional centralized estimation often requires global inference of the
covariance matrix, which can be computationally intensive in large dimensions.
Approximate inference based on message-passing algorithms, on the other hand,
can lead to unstable and biased estimation in loopy graphical models. In this
paper, we propose a general framework for distributed estimation based on a
maximum marginal likelihood (MML) approach. This approach computes local
parameter estimates by maximizing marginal likelihoods defined with respect to
data collected from local neighborhoods. Due to the non-convexity of the MML
problem, we introduce and solve a convex relaxation. The local estimates are
then combined into a global estimate without the need for iterative
message-passing between neighborhoods. The proposed algorithm is naturally
parallelizable and computationally efficient, thereby making it suitable for
high-dimensional problems. In the classical regime where the number of
variables $p$ is fixed and the number of samples $T$ increases to infinity, the
proposed estimator is shown to be asymptotically consistent and to improve
monotonically as the local neighborhood size increases. In the high-dimensional
scaling regime where both $p$ and $T$ increase to infinity, the convergence
rate to the true parameters is derived and is seen to be comparable to
centralized maximum likelihood estimation. Extensive numerical experiments
demonstrate the improved performance of the two-hop version of the proposed
estimator, which suffices to almost close the gap to the centralized maximum
likelihood estimator at a reduced computational cost.
The New Approach on Fuzzy Decision Trees
Authors: Jooyeol Yun, Jun won Seo, Taeseon Yoon
Date: 2014-08-13 00:00:00
Tasks: BIG-bench Machine Learning

Decision trees have been widely used in machine learning. However, due to
some reasons, data collecting in real world contains a fuzzy and uncertain
form. The decision tree should be able to handle such fuzzy data. This paper
presents a method to construct fuzzy decision tree. It proposes a fuzzy
decision tree induction method in iris flower data set, obtaining the entropy
from the distance between an average value and a particular value. It also
presents an experiment result that shows the accuracy compared to former ID3.
An Improved Approach for Contrast Enhancement of Spinal Cord Images based on Multiscale Retinex Algorithm
Authors: Sreenivasa Setty, N. K Srinath, M. C. Hanumantharaju
Date: 2014-08-13 00:00:00

This paper presents a new approach for contrast enhancement of spinal cord
medical images based on multirate scheme incorporated into multiscale retinex
algorithm. The proposed work here uses HSV color space, since HSV color space
separates color details from intensity. The enhancement of medical image is
achieved by down sampling the original image into five versions, namely, tiny,
small, medium, fine, and normal scale. This is due to the fact that the each
versions of the image when independently enhanced and reconstructed results in
enormous improvement in the visual quality. Further, the contrast stretching
and MultiScale Retinex (MSR) techniques are exploited in order to enhance each
of the scaled version of the image. Finally, the enhanced image is obtained by
combining each of these scales in an efficient way to obtain the composite
enhanced image. The efficiency of the proposed algorithm is validated by using
a wavelet energy metric in the wavelet domain. Reconstructed image using
proposed method highlights the details (edges and tissues), reduces image noise
(Gaussian and Speckle) and improves the overall contrast. The proposed
algorithm also enhances sharp edges of the tissue surrounding the spinal cord
regions which is useful for diagnosis of spinal cord lesions. Elaborated
experiments are conducted on several medical images and results presented show
that the enhanced medical pictures are of good quality and is found to be
better compared with other researcher methods.
Learning Multi-Scale Representations for Material Classification
Authors: Wenbin Li, Mario Fritz
Date: 2014-08-13 00:00:00
Tasks: Classification, General Classification, Material Classification, Material Recognition, Object Recognition

The recent progress in sparse coding and deep learning has made unsupervised
feature learning methods a strong competitor to hand-crafted descriptors. In
computer vision, success stories of learned features have been predominantly
reported for object recognition tasks. In this paper, we investigate if and how
feature learning can be used for material recognition. We propose two
strategies to incorporate scale information into the learning procedure
resulting in a novel multi-scale coding procedure. Our results show that our
learned features for material recognition outperform hand-crafted descriptors
on the FMD and the KTH-TIPS2 material classification benchmarks.
Hashing for Similarity Search: A Survey
Authors: Jingdong Wang, Heng Tao Shen, Jingkuan Song, Jianqiu Ji
Date: 2014-08-13 00:00:00
Tasks: Survey

Similarity search (nearest neighbor search) is a problem of pursuing the data
items whose distances to a query item are the smallest from a large database.
Various methods have been developed to address this problem, and recently a lot
of efforts have been devoted to approximate search. In this paper, we present a
survey on one of the main solutions, hashing, which has been widely studied
since the pioneering work locality sensitive hashing. We divide the hashing
algorithms two main categories: locality sensitive hashing, which designs hash
functions without exploring the data distribution and learning to hash, which
learns hash functions according the data distribution, and review them from
various aspects, including hash function design and distance measure and search
scheme in the hash coding space.
Linear Contour Learning: A Method for Supervised Dimension Reduction
Authors: Bing Li, Hongyuan Zha, Francesca Chiaromonte
Date: 2014-08-13 00:00:00
Tasks: Dimensionality Reduction, regression

We propose a novel approach to sufficient dimension reduction in regression,
based on estimating contour directions of negligible variation for the response
surface. These directions span the orthogonal complement of the minimal space
relevant for the regression, and can be extracted according to a measure of the
variation in the response, leading to General Contour Regression(GCR). In
comparison to exiisting sufficient dimension reduction techniques, this
sontour-based mothology guarantees exhaustive estimation of the central space
under ellipticity of the predictoor distribution and very mild additional
assumptions, while maintaining vn-consisytency and somputational ease.
Moreover, it proves to be robust to departures from ellipticity. We also
establish some useful population properties for GCR. Simulations to compare
performance with that of standard techniques such as ordinary least squares,
sliced inverse regression, principal hessian directions, and sliced average
variance estimation confirm the advntages anticipated by theoretical analyses.
We also demonstrate the use of contour-based methods on a data set concerning
grades of students from Massachusetts colleges.
Robust OS-ELM with a novel selective ensemble based on particle swarm optimization
Authors: Yang Liu, Bo He, Diya Dong, Yue Shen, Tianhong Yan, Rui Nian, Amaury Lendase
Date: 2014-08-13 00:00:00
Tasks: General Classification

In this paper, a robust online sequential extreme learning machine (ROS-ELM)
is proposed. It is based on the original OS-ELM with an adaptive selective
ensemble framework. Two novel insights are proposed in this paper. First, a
novel selective ensemble algorithm referred to as particle swarm optimization
selective ensemble (PSOSEN) is proposed. Noting that PSOSEN is a general
selective ensemble method which is applicable to any learning algorithms,
including batch learning and online learning. Second, an adaptive selective
ensemble framework for online learning is designed to balance the robustness
and complexity of the algorithm. Experiments for both regression and
classification problems with UCI data sets are carried out. Comparisons between
OS-ELM, simple ensemble OS-ELM (EOS-ELM) and the proposed ROS-ELM empirically
show that ROS-ELM significantly improves the robustness and stability.
A Classifier-free Ensemble Selection Method based on Data Diversity in Random Subspaces
Authors: Albert H. R. Ko, Robert Sabourin, Alceu S. Britto Jr, Luiz E. S. Oliveira
Date: 2014-08-13 00:00:00
Tasks: Clustering, Diversity

The Ensemble of Classifiers (EoC) has been shown to be effective in improving
the performance of single classifiers by combining their outputs, and one of
the most important properties involved in the selection of the best EoC from a
pool of classifiers is considered to be classifier diversity. In general,
classifier diversity does not occur randomly, but is generated systematically
by various ensemble creation methods. By using diverse data subsets to train
classifiers, these methods can create diverse classifiers for the EoC. In this
work, we propose a scheme to measure data diversity directly from random
subspaces, and explore the possibility of using it to select the best data
subsets for the construction of the EoC. Our scheme is the first ensemble
selection method to be presented in the literature based on the concept of data
diversity. Its main advantage over the traditional framework (ensemble creation
then selection) is that it obviates the need for classifier training prior to
ensemble selection. A single Genetic Algorithm (GA) and a Multi-Objective
Genetic Algorithm (MOGA) were evaluated to search for the best solutions for
the classifier-free ensemble selection. In both cases, objective functions
based on different clustering diversity measures were implemented and tested.
All the results obtained with the proposed classifier-free ensemble selection
method were compared with the traditional classifier-based ensemble selection
using Mean Classifier Error (ME) and Majority Voting Error (MVE). The
applicability of the method is tested on UCI machine learning problems and NIST
SD19 handwritten numerals.
Cluster based RBF Kernel for Support Vector Machines
Authors: Wojciech Marian Czarnecki, Jacek Tabor
Date: 2014-08-12 00:00:00
Tasks: General Classification

In the classical Gaussian SVM classification we use the feature space
projection transforming points to normal distributions with fixed covariance
matrices (identity in the standard RBF and the covariance of the whole dataset
in Mahalanobis RBF). In this paper we add additional information to Gaussian
SVM by considering local geometry-dependent feature space projection. We
emphasize that our approach is in fact an algorithm for a construction of the
new Gaussian-type kernel.
  We show that better (compared to standard RBF and Mahalanobis RBF)
classification results are obtained in the simple case when the space is
preliminary divided by k-means into two sets and points are represented as
normal distributions with a covariances calculated according to the dataset
partitioning.
  We call the constructed method C$_k$RBF, where $k$ stands for the amount of
clusters used in k-means. We show empirically on nine datasets from UCI
repository that C$_2$RBF increases the stability of the grid search (measured
as the probability of finding good parameters).