pwc-archive/papers-with-abstracts huggingface dataset:
A Comprehensive Approach to Universal Piecewise Nonlinear Regression Based on Trees
Authors: N. Denizcan Vanli, Suleyman S. Kozat
Date: 2013-11-25 00:00:00
Tasks: regression

In this paper, we investigate adaptive nonlinear regression and introduce
tree based piecewise linear regression algorithms that are highly efficient and
provide significantly improved performance with guaranteed upper bounds in an
individual sequence manner. We use a tree notion in order to partition the
space of regressors in a nested structure. The introduced algorithms adapt not
only their regression functions but also the complete tree structure while
achieving the performance of the "best" linear mixture of a doubly exponential
number of partitions, with a computational complexity only polynomial in the
number of nodes of the tree. While constructing these algorithms, we also avoid
using any artificial "weighting" of models (with highly data dependent
parameters) and, instead, directly minimize the final regression error, which
is the ultimate performance goal. The introduced methods are generic such that
they can readily incorporate different tree construction methods such as random
trees in their framework and can use different regressor or partitioning
functions as demonstrated in the paper.
Quality Estimation of English-Hindi Outputs using Naive Bayes Classifier
Authors: Rashmi Gupta, Nisheeth Joshi, Iti Mathur
Date: 2013-12-27 00:00:00
Tasks: Machine Translation, Translation

In this paper we present an approach for estimating the quality of machine
translation system. There are various methods for estimating the quality of
output sentences, but in this paper we focus on Na\"ive Bayes classifier to
build model using features which are extracted from the input sentences. These
features are used for finding the likelihood of each of the sentences of the
training data which are then further used for determining the scores of the
test data. On the basis of these scores we determine the class labels of the
test data.
Sub-Classifier Construction for Error Correcting Output Code Using Minimum Weight Perfect Matching
Authors: Patoomsiri Songsiri, Thimaporn Phetkaew, Ryutaro Ichise, Boonserm Kijsirikul
Date: 2013-12-27 00:00:00
Tasks: Classification, General Classification, Multi-class Classification

Multi-class classification is mandatory for real world problems and one of
promising techniques for multi-class classification is Error Correcting Output
Code. We propose a method for constructing the Error Correcting Output Code to
obtain the suitable combination of positive and negative classes encoded to
represent binary classifiers. The minimum weight perfect matching algorithm is
applied to find the optimal pairs of subset of classes by using the
generalization performance as a weighting criterion. Based on our method, each
subset of classes with positive and negative labels is appropriately combined
for learning the binary classifiers. Experimental results show that our
technique gives significantly higher performance compared to traditional
methods including the dense random code and the sparse random code both in
terms of accuracy and classification times. Moreover, our method requires
significantly smaller number of binary classifiers while maintaining accuracy
compared to the One-Versus-One.
Near-separable Non-negative Matrix Factorization with $\ell_1$- and Bregman Loss Functions
Authors: Abhishek Kumar, Vikas Sindhwani
Date: 2013-12-27 00:00:00

Recently, a family of tractable NMF algorithms have been proposed under the
assumption that the data matrix satisfies a separability condition Donoho &
Stodden (2003); Arora et al. (2012). Geometrically, this condition reformulates
the NMF problem as that of finding the extreme rays of the conical hull of a
finite set of vectors. In this paper, we develop several extensions of the
conical hull procedures of Kumar et al. (2013) for robust ($\ell_1$)
approximations and Bregman divergences. Our methods inherit all the advantages
of Kumar et al. (2013) including scalability and noise-tolerance. We show that
on foreground-background separation problems in computer vision, robust
near-separable NMFs match the performance of Robust PCA, considered state of
the art on these problems, with an order of magnitude faster training time. We
also demonstrate applications in exemplar selection settings.
Lesion Border Detection in Dermoscopy Images Using Ensembles of Thresholding Methods
Authors: M. Emre Celebi, Quan Wen, Sae Hwang, Hitoshi Iyatomi, Gerald Schaefer
Date: 2013-12-26 00:00:00

Dermoscopy is one of the major imaging modalities used in the diagnosis of
melanoma and other pigmented skin lesions. Due to the difficulty and
subjectivity of human interpretation, automated analysis of dermoscopy images
has become an important research area. Border detection is often the first step
in this analysis. In many cases, the lesion can be roughly separated from the
background skin using a thresholding method applied to the blue channel.
However, no single thresholding method appears to be robust enough to
successfully handle the wide variety of dermoscopy images encountered in
clinical practice. In this paper, we present an automated method for detecting
lesion borders in dermoscopy images using ensembles of thresholding methods.
Experiments on a difficult set of 90 images demonstrate that the proposed
method is robust, fast, and accurate when compared to nine state-of-the-art
methods.
Finding More Relevance: Propagating Similarity on Markov Random Field for Image Retrieval
Authors: Peng Lu, Xujun Peng, Xinshan Zhu, Xiaojie Wang
Date: 2013-12-26 00:00:00
Tasks: Image Retrieval, Retrieval

To effectively retrieve objects from large corpus with high accuracy is a
challenge task. In this paper, we propose a method that propagates visual
feature level similarities on a Markov random field (MRF) to obtain a high
level correspondence in image space for image pairs. The proposed
correspondence between image pair reflects not only the similarity of low-level
visual features but also the relations built through other images in the
database and it can be easily integrated into the existing
bag-of-visual-words(BoW) based systems to reduce the missing rate. We evaluate
our method on the standard Oxford-5K, Oxford-105K and Paris-6K dataset. The
experiment results show that the proposed method significantly improves the
retrieval accuracy on three datasets and exceeds the current state-of-the-art
retrieval performance.
(More) Efficient Reinforcement Learning via Posterior Sampling
Authors: Ian Osband, Daniel Russo, Benjamin Van Roy
Date: 2013-06-04 00:00:00
Proceeding: NeurIPS 2013 12
Tasks: Efficient Exploration, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Most provably-efficient learning algorithms introduce optimism about
poorly-understood states and actions to encourage exploration. We study an
alternative approach for efficient exploration, posterior sampling for
reinforcement learning (PSRL). This algorithm proceeds in repeated episodes of
known duration. At the start of each episode, PSRL updates a prior distribution
over Markov decision processes and takes one sample from this posterior. PSRL
then follows the policy that is optimal for this sample during the episode. The
algorithm is conceptually simple, computationally efficient and allows an agent
to encode prior knowledge in a natural way. We establish an $\tilde{O}(\tau S
\sqrt{AT})$ bound on the expected regret, where $T$ is time, $\tau$ is the
episode length and $S$ and $A$ are the cardinalities of the state and action
spaces. This bound is one of the first for an algorithm not based on optimism,
and close to the state of the art for any reinforcement learning algorithm. We
show through simulation that PSRL significantly outperforms existing algorithms
with similar regret bounds.
Top Down Approach to Multiple Plane Detection
Authors: Prateek Singhal, Aditya Deshpande, N. Dinesh Reddy, K. Madhava Krishna
Date: 2013-12-23 00:00:00
Tasks: General Classification

Detecting multiple planes in images is a challenging problem, but one with
many applications. Recent work such as J-Linkage and Ordered Residual Kernels
have focussed on developing a domain independent approach to detect multiple
structures. These multiple structure detection methods are then used for
estimating multiple homographies given feature matches between two images.
Features participating in the multiple homographies detected, provide us the
multiple scene planes. We show that these methods provide locally optimal
results and fail to merge detected planar patches to the true scene planes.
These methods use only residues obtained on applying homography of one plane to
another as cue for merging. In this paper, we develop additional cues such as
local consistency of planes, local normals, texture etc. to perform better
classification and merging . We formulate the classification as an MRF problem
and use TRWS message passing algorithm to solve non metric energy terms and
complex sparse graph structure. We show results on challenging dataset common
in robotics navigation scenarios where our method shows accuracy of more than
85 percent on average while being close or same as the actual number of scene
planes.
Shape-constrained Estimation of Value Functions
Authors: Mohammad Mousavi, Peter W. Glynn
Date: 2013-12-26 00:00:00

We present a fully nonparametric method to estimate the value function, via
simulation, in the context of expected infinite-horizon discounted rewards for
Markov chains. Estimating such value functions plays an important role in
approximate dynamic programming and applied probability in general. We
incorporate "soft information" into the estimation algorithm, such as knowledge
of convexity, monotonicity, or Lipchitz constants. In the presence of such
information, a nonparametric estimator for the value function can be computed
that is provably consistent as the simulated time horizon tends to infinity. As
an application, we implement our method on price tolling agreement contracts in
energy markets.
Model-based clustering with Hidden Markov Model regression for time series with regime changes
Authors: Faicel Chamroukhi, Allou Samé, Patrice Aknin, Gérard Govaert
Date: 2013-12-25 00:00:00
Tasks: Clustering, model, parameter estimation, regression, Time Series, Time Series Analysis, Time Series Clustering

This paper introduces a novel model-based clustering approach for clustering
time series which present changes in regime. It consists of a mixture of
polynomial regressions governed by hidden Markov chains. The underlying hidden
process for each cluster activates successively several polynomial regimes
during time. The parameter estimation is performed by the maximum likelihood
method through a dedicated Expectation-Maximization (EM) algorithm. The
proposed approach is evaluated using simulated time series and real-world time
series issued from a railway diagnosis application. Comparisons with existing
approaches for time series clustering, including the stand EM for Gaussian
mixtures, $K$-means clustering, the standard mixture of regression models and
mixture of Hidden Markov Models, demonstrate the effectiveness of the proposed
approach.
Robust EM algorithm for model-based curve clustering
Authors: Faicel Chamroukhi
Date: 2013-12-25 00:00:00
Tasks: Clustering, Density Estimation, model, regression

Model-based clustering approaches concern the paradigm of exploratory data
analysis relying on the finite mixture model to automatically find a latent
structure governing observed data. They are one of the most popular and
successful approaches in cluster analysis. The mixture density estimation is
generally performed by maximizing the observed-data log-likelihood by using the
expectation-maximization (EM) algorithm. However, it is well-known that the EM
algorithm initialization is crucial. In addition, the standard EM algorithm
requires the number of clusters to be known a priori. Some solutions have been
provided in [31, 12] for model-based clustering with Gaussian mixture models
for multivariate data. In this paper we focus on model-based curve clustering
approaches, when the data are curves rather than vectorial data, based on
regression mixtures. We propose a new robust EM algorithm for clustering
curves. We extend the model-based clustering approach presented in [31] for
Gaussian mixture models, to the case of curve clustering by regression
mixtures, including polynomial regression mixtures as well as spline or
B-spline regressions mixtures. Our approach both handles the problem of
initialization and the one of choosing the optimal number of clusters as the EM
learning proceeds, rather than in a two-fold scheme. This is achieved by
optimizing a penalized log-likelihood criterion. A simulation study confirms
the potential benefit of the proposed algorithm in terms of robustness
regarding initialization and funding the actual number of clusters.
Mixture model-based functional discriminant analysis for curve classification
Authors: Faicel Chamroukhi, Hervé Glotin
Date: 2013-12-25 00:00:00
Tasks: Classification, General Classification, regression

Statistical approaches for Functional Data Analysis concern the paradigm for
which the individuals are functions or curves rather than finite dimensional
vectors. In this paper, we particularly focus on the modeling and the
classification of functional data which are temporal curves presenting regime
changes over time. More specifically, we propose a new mixture model-based
discriminant analysis approach for functional data using a specific hidden
process regression model. Our approach is particularly adapted to both handle
the problem of complex-shaped classes of curves, where each class is composed
of several sub-classes, and to deal with the regime changes within each
homogeneous sub-class. The model explicitly integrates the heterogeneity of
each class of curves via a mixture model formulation, and the regime changes
within each sub-class through a hidden logistic process. The approach allows
therefore for fitting flexible curve-models to each class of complex-shaped
curves presenting regime changes through an unsupervised learning scheme, to
automatically summarize it into a finite number of homogeneous clusters, each
of them is decomposed into several regimes. The model parameters are learned by
maximizing the observed-data log-likelihood for each class by using a dedicated
expectation-maximization (EM) algorithm. Comparisons on simulated data and real
data with alternative approaches, including functional linear discriminant
analysis and functional mixture discriminant analysis with polynomial
regression mixtures and spline regression mixtures, show that the proposed
approach provides better results regarding the discrimination results and
significantly improves the curves approximation.
Classification automatique de données temporelles en classes ordonnées
Authors: Faicel Chamroukhi, Allou Samé, Gérard Govaert, Patrice Aknin
Date: 2013-12-25 00:00:00
Tasks: Classification, General Classification

This paper proposes a method of segmenting temporal data into ordered
classes. It is based on mixture models and a discrete latent process, which
enables to successively activates the classes. The classification can be
performed by maximizing the likelihood via the EM algorithm or by
simultaneously optimizing the model parameters and the partition by the CEM
algorithm. These two algorithms can be seen as alternatives to Fisher's
algorithm, which improve its computing time.
Functional Mixture Discriminant Analysis with hidden process regression for curve classification
Authors: Faicel Chamroukhi, Heré Glotin, Céline Rabouy
Date: 2013-12-25 00:00:00
Tasks: General Classification, regression

We present a new mixture model-based discriminant analysis approach for
functional data using a specific hidden process regression model. The approach
allows for fitting flexible curve-models to each class of complex-shaped curves
presenting regime changes. The model parameters are learned by maximizing the
observed-data log-likelihood for each class by using a dedicated
expectation-maximization (EM) algorithm. Comparisons on simulated data with
alternative approaches show that the proposed approach provides better results.
Supervised learning of a regression model based on latent process. Application to the estimation of fuel cell life time
Authors: Raïssa Onanena, Faicel Chamroukhi, Latifa Oukhellou, Denis Candusso, Patrice Aknin, Daniel Hissel
Date: 2013-12-25 00:00:00
Tasks: regression

This paper describes a pattern recognition approach aiming to estimate fuel
cell duration time from electrochemical impedance spectroscopy measurements. It
consists in first extracting features from both real and imaginary parts of the
impedance spectrum. A parametric model is considered in the case of the real
part, whereas regression model with latent variables is used in the latter
case. Then, a linear regression model using different subsets of extracted
features is used fo r the estimation of fuel cell time duration. The
performances of the proposed approach are evaluated on experimental data set to
show its feasibility. This could lead to interesting perspectives for
predictive maintenance policy of fuel cell.
A regression model with a hidden logistic process for feature extraction from time series
Authors: Faicel Chamroukhi, Allou Samé, Gérard Govaert, Patrice Aknin
Date: 2013-12-25 00:00:00
Tasks: regression, Time Series, Time Series Analysis

A new approach for feature extraction from time series is proposed in this
paper. This approach consists of a specific regression model incorporating a
discrete hidden logistic process. The model parameters are estimated by the
maximum likelihood method performed by a dedicated Expectation Maximization
(EM) algorithm. The parameters of the hidden logistic process, in the inner
loop of the EM algorithm, are estimated using a multi-class Iterative
Reweighted Least-Squares (IRLS) algorithm. A piecewise regression algorithm and
its iterative variant have also been considered for comparisons. An
experimental study using simulated and real data reveals good performances of
the proposed approach.
A New Approach to Constraint Weight Learning for Variable Ordering in CSPs
Authors: Muhammad Rezaul Karim
Date: 2013-12-25 00:00:00

A Constraint Satisfaction Problem (CSP) is a framework used for modeling and
solving constrained problems. Tree-search algorithms like backtracking try to
construct a solution to a CSP by selecting the variables of the problem one
after another. The order in which these algorithm select the variables
potentially have significant impact on the search performance. Various
heuristics have been proposed for choosing good variable ordering. Many
powerful variable ordering heuristics weigh the constraints first and then
utilize the weights for selecting good order of the variables. Constraint
weighting are basically employed to identify global bottlenecks in a CSP.
  In this paper, we propose a new approach for learning weights for the
constraints using competitive coevolutionary Genetic Algorithm (GA). Weights
learned by the coevolutionary GA later help to make better choices for the
first few variables in a search. In the competitive coevolutionary GA,
constraints and candidate solutions for a CSP evolve together through an
inverse fitness interaction process. We have conducted experiments on several
random, quasi-random and patterned instances to measure the efficiency of the
proposed approach. The results and analysis show that the proposed approach is
good at learning weights to distinguish the hard constraints for quasi-random
instances and forced satisfiable random instances generated with the Model RB.
For other type of instances, RNDI still seems to be the best approach as our
experiments show.
A regression model with a hidden logistic process for signal parametrization
Authors: Faicel Chamroukhi, Allou Samé, Gérard Govaert, Patrice Aknin
Date: 2013-12-25 00:00:00
Tasks: regression

A new approach for signal parametrization, which consists of a specific
regression model incorporating a discrete hidden logistic process, is proposed.
The model parameters are estimated by the maximum likelihood method performed
by a dedicated Expectation Maximization (EM) algorithm. The parameters of the
hidden logistic process, in the inner loop of the EM algorithm, are estimated
using a multi-class Iterative Reweighted Least-Squares (IRLS) algorithm. An
experimental study using simulated and real data reveals good performances of
the proposed approach.
Parallel Computation Is ESS
Authors: Nabarun Mondal, Partha P. Ghosh
Date: 2013-03-31 00:00:00

There are enormous amount of examples of Computation in nature, exemplified
across multiple species in biology. One crucial aim for these computations
across all life forms their ability to learn and thereby increase the chance of
their survival. In the current paper a formal definition of autonomous learning
is proposed. From that definition we establish a Turing Machine model for
learning, where rule tables can be added or deleted, but can not be modified.
Sequential and parallel implementations of this model are discussed. It is
found that for general purpose learning based on this model, the
implementations capable of parallel execution would be evolutionarily stable.
This is proposed to be of the reasons why in Nature parallelism in computation
is found in abundance.
Systems Theoretic Techniques for Modeling, Control, and Decision Support in Complex Dynamic Systems
Authors: Armen Bagdasaryan
Date: 2010-08-04 00:00:00
Tasks: Decision Making

We discuss the problems of modeling, control, and decision support in complex
dynamic systems from a general system theoretic point of view. The main
characteristics of complex systems and of system approach to complex system
study are considered. We provide an overview and analysis of known existing
paradigms and methods of mathematical modeling and simulation of complex
systems, which support the processes of control and decision making. Then we
continue with the general dynamic modeling and simulation technique for complex
hierarchical systems functioning in control loop. Architectural and structural
models of computer information system intended for simulation and decision
support in complex systems are presented.
Modèle à processus latent et algorithme EM pour la régression non linéaire
Authors: Faicel Chamroukhi, Allou Samé, Gérard Govaert, Patrice Aknin
Date: 2013-12-25 00:00:00
Tasks: regression

A non linear regression approach which consists of a specific regression
model incorporating a latent process, allowing various polynomial regression
models to be activated preferentially and smoothly, is introduced in this
paper. The model parameters are estimated by maximum likelihood performed via a
dedicated expecation-maximization (EM) algorithm. An experimental study using
simulated and real data sets reveals good performances of the proposed
approach.