JMLR 2005

73 papers

A Bayes Optimal Approach for Partitioning the Values of Categorical Attributes Marc Boullé

A Bayesian Model for Supervised Clustering with the Dirichlet Process Prior Hal Daumé Iii, Daniel Marcu

A Classification Framework for Anomaly Detection Ingo Steinwart, Don Hush, Clint Scovel

A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data Rie Kubota Ando, Tong Zhang

A Generalization Error for Q-Learning Susan A. Murphy

A Modified Finite Newton Method for Fast Solution of Large Scale Linear SVMs S. Sathiya Keerthi, Dennis DeCoste

A Unifying View of Sparse Approximate Gaussian Process Regression Joaquin Quiñonero-Candela, Carl Edward Rasmussen

Active Coevolutionary Learning of Deterministic Finite Automata Josh Bongard, Hod Lipson

Active Learning to Recognize Multiple Types of Plankton Tong Luo, Kurt Kramer, Dmitry B. Goldgof, Lawrence O. Hall, Scott Samson, Andrew Remsen, Thomas Hopkins

Adaptive Online Prediction by Following the Perturbed Leader Marcus Hutter, Jan Poland

Algorithmic Stability and Meta-Learning Andreas Maurer

An MDP-Based Recommender System Guy Shani, David Heckerman, Ronen I. Brafman

Analysis of Variance of Cross-Validation Estimators of the Generalization Error Marianthi Markatou, Hong Tian, Shameek Biswas, George Hripcsak

Assessing Approximate Inference for Binary Gaussian Process Classification Malte Kuss, Carl Edward Rasmussen

Asymptotic Model Selection for Naive Bayesian Networks Dmitry Rusakov, Dan Geiger

Asymptotics in Empirical Risk Minimization Leila Mohammadi, Sara van de Geer

Change Point Problems in Linear Dynamical Systems Onno Zoeter, Tom Heskes

Characterization of a Family of Algorithms for Generalized Discriminant Analysis on Undersampled Problems Jieping Ye

Clustering on the Unit Hypersphere Using Von Mises-Fisher Distributions Arindam Banerjee, Inderjit S. Dhillon, Joydeep Ghosh, Suvrit Sra

Clustering with Bregman Divergences Arindam Banerjee, Srujana Merugu, Inderjit S. Dhillon, Joydeep Ghosh

Combining Information Extraction Systems Using Voting and Stacked Generalization Georgios Sigletos, Georgios Paliouras, Constantine D. Spyropoulos, Michalis Hatzopoulos

Concentration Bounds for Unigram Language Models Evgeny Drukh, Yishay Mansour

Convergence Theorems for Generalized Alternating Minimization Procedures Asela Gunawardana, William Byrne

Core Vector Machines: Fast SVM Training on Very Large Data Sets Ivor W. Tsang, James T. Kwok, Pak-Ming Cheung

Denoising Source Separation Jaakko Särelä, Harri Valpola

Diffusion Kernels on Statistical Manifolds John Lafferty, Guy Lebanon

Dimension Reduction in Text Classification with Support Vector Machines Hyunsoo Kim, Peg Howland, Haesun Park

Efficient Computation of Gapped Substring Kernels on Large Alphabets Juho Rousu, John Shawe-Taylor

Efficient Margin Maximizing with Boosting Gunnar Rätsch, Manfred K. Warmuth

Estimating Functions for Blind Separation When Sources Have Variance Dependencies Motoaki Kawanabe, Klaus-Robert Müller

Estimation of Non-Normalized Statistical Models by Score Matching Aapo Hyvärinen

Expectation Consistent Approximate Inference Manfred Opper, Ole Winther

Fast Kernel Classifiers with Online and Active Learning Antoine Bordes, Seyda Ertekin, Jason Weston, Léon Bottou

Feature Selection for Unsupervised and Supervised Inference: The Emergence of Sparsity in a Weight-Based Approach Lior Wolf, Amnon Shashua

Frames, Reproducing Kernels, Regularization and Learning Alain Rakotomamonjy, Stéphane Canu

Gaussian Processes for Ordinal Regression Wei Chu, Zoubin Ghahramani

Generalization Bounds and Complexities Based on Sparsity and Clustering for Convex Combinations of Functions from Random Classes Savina Andonova Jaeger

Generalization Bounds for the Area Under the ROC Curve Shivani Agarwal, Thore Graepel, Ralf Herbrich, Sariel Har-Peled, Dan Roth

Information Bottleneck for Gaussian Variables Gal Chechik, Amir Globerson, Naftali Tishby, Yair Weiss

Inner Product Spaces for Bayesian Networks Atsuyoshi Nakamura, Michael Schmitt, Niels Schmitt, Hans Ulrich Simon

Kernel Methods for Measuring Independence Arthur Gretton, Ralf Herbrich, Alexander Smola, Olivier Bousquet, Bernhard Schölkopf

Large Margin Methods for Structured and Interdependent Output Variables Ioannis Tsochantaridis, Thorsten Joachims, Thomas Hofmann, Yasemin Altun

Learning a Mahalanobis Metric from Equivalence Constraints Aharon Bar-Hillel, Tomer Hertz, Noam Shental, Daphna Weinshall

Learning from Examples as an Inverse Problem Ernesto De Vito, Lorenzo Rosasco, Andrea Caponnetto, Umberto De Giovannini, Francesca Odone

Learning Hidden Variable Networks: The Information Bottleneck Approach Gal Elidan, Nir Friedman

Learning Module Networks Eran Segal, Dana Pe'er, Aviv Regev, Daphne Koller, Nir Friedman

Learning Multiple Tasks with Kernel Methods Theodoros Evgeniou, Charles A. Micchelli, Massimiliano Pontil

Learning the Kernel Function via Regularization Charles A. Micchelli, Massimiliano Pontil

Learning the Kernel with Hyperkernels Cheng Soon Ong, Alexander J. Smola, Robert C. Williamson

Learning with Decision Lists of Data-Dependent Features Mario Marchand, Marina Sokolova

Local Propagation in Conditional Gaussian Bayesian Networks Robert G. Cowell

Loopy Belief Propagation: Convergence and Effects of Message Errors Alexander T. Ihler, John W. Fisher Iii, Alan S. Willsky

Machine Learning Methods for Predicting Failures in Hard Drives: A Multiple-Instance Application Joseph F. Murray, Gordon F. Hughes, Kenneth Kreutz-Delgado

Managing Diversity in Regression Ensembles Gavin Brown, Jeremy L. Wyatt, Peter Tiňo

Matrix Exponentiated Gradient Updates for On-Line Learning and Bregman Projection Koji Tsuda, Gunnar Rätsch, Manfred K. Warmuth

Maximum Margin Algorithms with Boolean Kernels Roni Khardon, Rocco A. Servedio

Multiclass Boosting for Weak Classifiers Günther Eibl, Karl-Peter Pfeiffer

Multiclass Classification with Multi-Prototype Support Vector Machines Fabio Aiolli, Alessandro Sperduti

New Horn Revision Algorithms Judy Goldsmith, Robert H. Sloan

On the Nystrom Method for Approximating a Gram Matrix for Improved Kernel-Based Learning Petros Drineas, Michael W. Mahoney

Prioritization Methods for Accelerating MDP Solvers David Wingate, Kevin D. Seppi

Probabilistic Non-Linear Principal Component Analysis with Gaussian Process Latent Variable Models Neil Lawrence

Quasi-Geodesic Neural Learning Algorithms over the Orthogonal Group: A Tutorial Simone Fiori

Semigroup Kernels on Measures Marco Cuturi, Kenji Fukumizu, Jean-Philippe Vert

Separating a Real-Life Nonlinear Image Mixture Luís B. Almeida

Smooth Ε-Insensitive Regression by Loss Symmetrization Ofer Dekel, Shai Shalev-Shwartz, Yoram Singer

Stability of Randomized Learning Algorithms Andre Elisseeff, Theodoros Evgeniou, Massimiliano Pontil

Tree-Based Batch Mode Reinforcement Learning Damien Ernst, Pierre Geurts, Louis Wehenkel

Tutorial on Practical Prediction Theory for Classification John Langford

Universal Algorithms for Learning Theory Part I : Piecewise Constant Functions Peter Binev, Albert Cohen, Wolfgang Dahmen, Ronald DeVore, Vladimir Temlyakov

Variational Message Passing John Winn, Christopher M. Bishop

What's Strange About Recent Events (WSARE): An Algorithm for the Early Detection of Disease Outbreaks Weng-Keen Wong, Andrew Moore, Gregory Cooper, Michael Wagner

Working Set Selection Using Second Order Information for Training Support Vector Machines Rong-En Fan, Pai-Hsuen Chen, Chih-Jen Lin