JMLR 2003

57 papers

A Generative Model for Separating Illumination and Reflectance from Images Inna Stainvas, David Lowe

A Maximum Likelihood Approach to Single-Channel Source Separation Gil-Jin Jang, Te-Won Lee

A Multiscale Framework for Blind Separation of Linearly Mixed Signals Pavel Kisilev, Michael Zibulevsky, Yehoshua Y. Zeevi

A Unified Framework for Model-Based Clustering Shi Zhong, Joydeep Ghosh

An Approximate Analytical Approach to Resampling Averages (Kernel Machines Section) Dörthe Malzahn, Manfred Opper

An Efficient Boosting Algorithm for Combining Preferences Yoav Freund, Raj Iyer, Robert E. Schapire, Yoram Singer

An Empirical Study of the Use of Relevance Information in Inductive Logic Programming Ashwin Srinivasan, Ross D. King, Michael E. Bain

Beyond Independent Components: Trees and Clusters Francis R. Bach, Michael I. Jordan

Blind Separation of Post-Nonlinear Mixtures Using Linearizing Transformations and Temporal Decorrelation Andreas Ziehe, Motoaki Kawanabe, Stefan Harmeling, Klaus-Robert Müller

Blind Source Recovery: A Framework in the State Space Khurram Waheed, Fathi M. Salem

Blind Source Separation via Generalized Eigenvalue Decomposition Lucas Parra, Paul Sajda

Bottom-up Relational Learning of Pattern Matching Rules for Information Extraction Mary Elaine Califf, Raymond J. Mooney

Combining Knowledge from Different Sources in Causal Probabilistic Models Marek J. Druzdzel, Francisco J. Díez

Comparing Bayes Model Averaging and Stacking When Model Approximation Error Cannot Be Ignored Bertrand Clarke

Concentration Inequalities for the Missing Mass and for Histogram Rule Error David McAllester, Luis Ortiz

Dependence, Correlation and Gaussianity in Independent Component Analysis Jean-François Cardoso

Designing Committees of Models Through Deliberate Weighting of Data Points Stefan W. Christensen, Ian Sinclair, Philippa A. S. Reed

Energy-Based Models for Sparse Overcomplete Representations Yee Whye Teh, Max Welling, Simon Osindero, Geoffrey E. Hinton

FINkNN: A Fuzzy Interval Number K-Nearest Neighbor Classifier for Prediction of Sugar Production from Populations of Samples Vassilios Petridis, Vassilis G. Kaburlasos

Fusion of Domain Knowledge with Data for Structural Learning in Object Oriented Domains Helge Langseth, Thomas D. Nielsen

Generalization Error Bounds for Bayesian Mixture Algorithms Ron Meir, Tong Zhang

Greedy Algorithms for Classification -- Consistency, Convergence Rates, and Adaptivity Shie Mannor, Ron Meir, Tong Zhang

ICA for Watermarking Digital Images Stéphane Bounkong, Borémi Toch, David Saad, David Lowe

ICA Using Spacings Estimates of Entropy Erik G. Learned-Miller, John W. Fisher Iii

ILP: A Short Look Back and a Longer Look Forward David Page, Ashwin Srinivasan

Inducing Grammars from Sparse Data Sets: A Survey of Algorithms and Results Orlando Cicchello, Stefan C. Kremer

Learning Behavior-Selection by Emotions and Cognition in a Multi-Goal Robot Task Sandra Clara Gadanho

Learning over Sets Using Kernel Principal Angles (Kernel Machines Section) Lior Wolf, Amnon Shashua

Learning Probabilistic Models: An Expected Utility Maximization Approach Craig Friedman, Sven Sandow

Learning Rates for Q-Learning Eyal Even Dar, Yishay Mansour

Learning Semantic Lexicons from a Part-of-Speech and Semantically Tagged Corpus Using Inductive Logic Programming Vincent Claveau, Pascale Sébillot, Cécile Fabre, Pierrette Bouillon

Least-Squares Policy Iteration Michail G. Lagoudakis, Ronald Parr

MISEP -- Linear and Nonlinear ICA Based on Mutual Information Luís B. Almeida

Nash Q-Learning for General-Sum Stochastic Games Junling Hu, Michael P. Wellman

On Inclusion-Driven Learning of Bayesian Networks Robert Castelo, Tomás Kocka

On Nearest-Neighbor Error-Correcting Output Codes with Application to All-Pairs Multiclass Support Vector Machines Aldebaro Klautau, Nikola Jevtić, Alon Orlitsky

On the Performance of Kernel Classes Shahar Mendelson

On the Proper Learning of Axis-Parallel Concepts Nader H. Bshouty, Lynn Burroughs

On the Rate of Convergence of Regularized Boosting Classifiers Gilles Blanchard, Gábor Lugosi, Nicolas Vayatis

Optimality of Universal Bayesian Sequence Prediction for General Loss and Alphabet Marcus Hutter

Optimally-Smooth Adaptive Boosting and Application to Agnostic Learning Dmitry Gavinsky

Overlearning in Marginal Distribution-Based ICA: Analysis and Solutions Jaakko Särelä, Ricardo Vigário

Path Kernels and Multiplicative Updates Eiji Takimoto, Manfred K. Warmuth

Preference Elicitation via Theory Refinement Peter Haddawy, Vu Ha, Angelo Restificar, Benjamin Geisler, John Miyamoto

Query Transformations for Improving the Efficiency of ILP Systems Vítor Santos Costa, Ashwin Srinivasan, Rui Camacho, Hendrik Blockeel, Bart Demoen, Gerda Janssens, Jan Struyf, Henk Vandecasteele, Wim Van Laer

Relational Learning as Search in a Critical Region Marco Botta, Attilio Giordana, Lorenza Saitta, Michèle Sebag

Smooth Boosting and Learning with Malicious Noise Rocco A. Servedio

Sparseness of Support Vector Machines Ingo Steinwart

Speedup Learning for Repair-Based Search by Identifying Redundant Steps Shaul Markovitch, Asaf Shatil

Statistical Dynamics of On-Line Independent Component Analysis Gleb Basalyga, Magnus Rattray

Task Clustering and Gating for Bayesian Multitask Learning Bart Bakker, Tom Heskes

The EM Algorithm for Kernel Matrix Completion with Auxiliary Data Koji Tsuda, Shotaro Akaho, Kiyoshi Asai

The Principled Design of Large-Scale Recursive Neural Network Architectures--DAG-RNNs and the Protein Structure Prediction Problem Pierre Baldi, Gianluca Pollastri

Think Globally, Fit Locally: Unsupervised Learning of Low Dimensional Manifolds Lawrence K. Saul, Sam T. Roweis

Tracking Linear-Threshold Concepts with Winnow Chris Mesterharm

Tree Induction vs. Logistic Regression: A Learning-Curve Analysis Claudia Perlich, Foster Provost, Jeffrey S. Simonoff

Tree-Structured Neural Decoding Christian d'Avignon, Donald Geman