JMLR Volume 6

Asymptotic Model Selection for Naive Bayesian Networks
Dmitry Rusakov, Dan Geiger; 6(Jan):1--35, 2005.
[abs][pdf]

Dimension Reduction in Text Classification with Support Vector Machines
Hyunsoo Kim, Peg Howland, Haesun Park; 6(Jan):37--53, 2005.
[abs][pdf]

Stability of Randomized Learning Algorithms
Andre Elisseeff, Theodoros Evgeniou, Massimiliano Pontil; 6(Jan):55--79, 2005.
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Learning Hidden Variable Networks: The Information Bottleneck Approach
Gal Elidan, Nir Friedman; 6(Jan):81--127, 2005.
[abs][pdf]

Diffusion Kernels on Statistical Manifolds
John Lafferty, Guy Lebanon; 6(Jan):129--163, 2005.
[abs][pdf]

Information Bottleneck for Gaussian Variables
Gal Chechik, Amir Globerson, Naftali Tishby, Yair Weiss; 6(Jan):165--188, 2005.
[abs][pdf]

Multiclass Boosting for Weak Classifiers
Günther Eibl, Karl-Peter Pfeiffer; 6(Feb):189--210, 2005.
[abs][pdf]

A Classification Framework for Anomaly Detection
Ingo Steinwart, Don Hush, Clint Scovel; 6(Feb):211--232, 2005.
[abs][pdf]

Denoising Source Separation
Jaakko Särelä, Harri Valpola; 6(Mar):233--272, 2005.
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Tutorial on Practical Prediction Theory for Classification
John Langford; 6(Mar):273--306, 2005.
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Generalization Bounds and Complexities Based on Sparsity and Clustering for Convex Combinations of Functions from Random Classes
Savina Andonova Jaeger; 6(Mar):307--340, 2005.
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A Modified Finite Newton Method for Fast Solution of Large Scale Linear SVMs
S. Sathiya Keerthi, Dennis DeCoste; 6(Mar):341--361, 2005.
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Core Vector Machines: Fast SVM Training on Very Large Data Sets
Ivor W. Tsang, James T. Kwok, Pak-Ming Cheung; 6(Apr):363--392, 2005.
[abs][pdf]

Generalization Bounds for the Area Under the ROC Curve
Shivani Agarwal, Thore Graepel, Ralf Herbrich, Sariel Har-Peled, Dan Roth; 6(Apr):393--425, 2005.
[abs][pdf]

Learning with Decision Lists of Data-Dependent Features
Mario Marchand, Marina Sokolova; 6(Apr):427--451, 2005.
[abs][pdf]

Estimating Functions for Blind Separation When Sources Have Variance Dependencies
Motoaki Kawanabe, Klaus-Robert Müller; 6(Apr):453--482, 2005.
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Characterization of a Family of Algorithms for Generalized Discriminant Analysis on Undersampled Problems
Jieping Ye; 6(Apr):483--502, 2005.
[abs][pdf]

Tree-Based Batch Mode Reinforcement Learning
Damien Ernst, Pierre Geurts, Louis Wehenkel; 6(Apr):503--556, 2005.
[abs][pdf]

Learning Module Networks
Eran Segal, Dana Pe'er, Aviv Regev, Daphne Koller, Nir Friedman; 6(Apr):557--588, 2005.
[abs][pdf]

Active Learning to Recognize Multiple Types of Plankton
Tong Luo, Kurt Kramer, Dmitry B. Goldgof, Lawrence O. Hall, Scott Samson, Andrew Remsen, Thomas Hopkins; 6(Apr):589--613, 2005.
[abs][pdf]

Learning Multiple Tasks with Kernel Methods
Theodoros Evgeniou, Charles A. Micchelli, Massimiliano Pontil; 6(Apr):615--637, 2005.
[abs][pdf]

Adaptive Online Prediction by Following the Perturbed Leader
Marcus Hutter, Jan Poland; 6(Apr):639--660, 2005.
[abs][pdf]

Variational Message Passing
John Winn, Christopher M. Bishop; 6(Apr):661--694, 2005.
[abs][pdf]

Estimation of Non-Normalized Statistical Models by Score Matching
Aapo Hyvärinen; 6(Apr):695--709, 2005.
[abs][pdf]

Smooth ε-Insensitive Regression by Loss Symmetrization
Ofer Dekel, Shai Shalev-Shwartz, Yoram Singer; 6(May):711--741, 2005.
[abs][pdf]

Quasi-Geodesic Neural Learning Algorithms Over the Orthogonal Group: A Tutorial
Simone Fiori; 6(May):743--781, 2005.
[abs][pdf]

Machine Learning Methods for Predicting Failures in Hard Drives: A Multiple-Instance Application
Joseph F. Murray, Gordon F. Hughes, Kenneth Kreutz-Delgado; 6(May):783--816, 2005.
[abs][pdf]

Multiclass Classification with Multi-Prototype Support Vector Machines
Fabio Aiolli, Alessandro Sperduti; 6(May):817--850, 2005.
[abs][pdf]

Prioritization Methods for Accelerating MDP Solvers
David Wingate, Kevin D. Seppi; 6(May):851--881, 2005.
[abs][pdf]

Learning from Examples as an Inverse Problem
Ernesto De Vito, Lorenzo Rosasco, Andrea Caponnetto, Umberto De Giovannini, Francesca Odone; 6(May):883--904, 2005.
[abs][pdf]

Loopy Belief Propagation: Convergence and Effects of Message Errors
Alexander T. Ihler, John W. Fisher III, Alan S. Willsky; 6(May):905--936, 2005.
[abs][pdf]

Learning a Mahalanobis Metric from Equivalence Constraints
Aharon Bar-Hillel, Tomer Hertz, Noam Shental, Daphna Weinshall; 6(Jun):937--965, 2005.
[abs][pdf]

Algorithmic Stability and Meta-Learning
Andreas Maurer; 6(Jun):967--994, 2005.
[abs][pdf]

Matrix Exponentiated Gradient Updates for On-line Learning and Bregman Projection
Koji Tsuda, Gunnar Rätsch, Manfred K. Warmuth; 6(Jun):995--1018, 2005.
[abs][pdf]

Gaussian Processes for Ordinal Regression
Wei Chu, Zoubin Ghahramani; 6(Jul):1019--1041, 2005.
[abs][pdf]

Learning the Kernel with Hyperkernels     (Kernel Machines Section)
Cheng Soon Ong, Alexander J. Smola, Robert C. Williamson; 6(Jul):1043--1071, 2005.
[abs][pdf]

A Generalization Error for Q-Learning
Susan A. Murphy; 6(Jul):1073--1097, 2005.
[abs][pdf]

Learning the Kernel Function via Regularization
Charles A. Micchelli, Massimiliano Pontil; 6(Jul):1099--1125, 2005.
[abs][pdf]

Analysis of Variance of Cross-Validation Estimators of the Generalization Error
Marianthi Markatou, Hong Tian, Shameek Biswas, George Hripcsak; 6(Jul):1127--1168, 2005.
[abs][pdf]

Semigroup Kernels on Measures
Marco Cuturi, Kenji Fukumizu, Jean-Philippe Vert; 6(Jul):1169--1198, 2005.
[abs][pdf]

Separating a Real-Life Nonlinear Image Mixture
Luís B. Almeida; 6(Jul):1199--1229, 2005.
[abs][pdf]

Concentration Bounds for Unigram Language Models
Evgeny Drukh, Yishay Mansour; 6(Aug):1231--1264, 2005.
[abs][pdf]

An MDP-Based Recommender System
Guy Shani, David Heckerman, Ronen I. Brafman; 6(Sep):1265--1295, 2005.
[abs][pdf]

Universal Algorithms for Learning Theory Part I : Piecewise Constant Functions
Peter Binev, Albert Cohen, Wolfgang Dahmen, Ronald DeVore, Vladimir Temlyakov; 6(Sep):1297--1321, 2005.
[abs][pdf]

Efficient Computation of Gapped Substring Kernels on Large Alphabets
Juho Rousu, John Shawe-Taylor; 6(Sep):1323--1344, 2005.
[abs][pdf]

Clustering on the Unit Hypersphere using von Mises-Fisher Distributions
Arindam Banerjee, Inderjit S. Dhillon, Joydeep Ghosh, Suvrit Sra; 6(Sep):1345--1382, 2005.
[abs][pdf]

Inner Product Spaces for Bayesian Networks
Atsuyoshi Nakamura, Michael Schmitt, Niels Schmitt, Hans Ulrich Simon; 6(Sep):1383--1403, 2005.
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Maximum Margin Algorithms with Boolean Kernels
Roni Khardon, Rocco A. Servedio; 6(Sep):1405--1429, 2005.
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A Bayes Optimal Approach for Partitioning the Values of Categorical Attributes
Marc Boullé; 6(Sep):1431--1452, 2005.
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Large Margin Methods for Structured and Interdependent Output Variables
Ioannis Tsochantaridis, Thorsten Joachims, Thomas Hofmann, Yasemin Altun; 6(Sep):1453--1484, 2005.
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Frames, Reproducing Kernels, Regularization and Learning
Alain Rakotomamonjy, Stéphane Canu; 6(Sep):1485--1515, 2005.
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Local Propagation in Conditional Gaussian Bayesian Networks
Robert G. Cowell; 6(Sep):1517--1550, 2005.
[abs][pdf]

A Bayesian Model for Supervised Clustering with the Dirichlet Process Prior
Hal Daumé III, Daniel Marcu; 6(Sep):1551--1577, 2005.
[abs][pdf]

Fast Kernel Classifiers with Online and Active Learning
Antoine Bordes, Seyda Ertekin, Jason Weston, Léon Bottou; 6(Sep):1579--1619, 2005.
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Managing Diversity in Regression Ensembles
Gavin Brown, Jeremy L. Wyatt, Peter Tiňo; 6(Sep):1621--1650, 2005.
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Active Coevolutionary Learning of Deterministic Finite Automata
Josh Bongard, Hod Lipson; 6(Oct):1651--1678, 2005.
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Assessing Approximate Inference for Binary Gaussian Process Classification
Malte Kuss, Carl Edward Rasmussen; 6(Oct):1679--1704, 2005.
[abs][pdf]

Clustering with Bregman Divergences
Arindam Banerjee, Srujana Merugu, Inderjit S. Dhillon, Joydeep Ghosh; 6(Oct):1705--1749, 2005.
[abs][pdf]

Combining Information Extraction Systems Using Voting and Stacked Generalization
Georgios Sigletos, Georgios Paliouras, Constantine D. Spyropoulos, Michalis Hatzopoulos; 6(Nov):1751--1782, 2005.
[abs][pdf]

Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models
Neil Lawrence; 6(Nov):1783--1816, 2005.
[abs][pdf]

A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data
Rie Kubota Ando, Tong Zhang; 6(Nov):1817--1853, 2005.
[abs][pdf]

Feature Selection for Unsupervised and Supervised Inference: The Emergence of Sparsity in a Weight-Based Approach
Lior Wolf, Amnon Shashua; 6(Nov):1855--1887, 2005.
[abs][pdf]

Working Set Selection Using Second Order Information for Training Support Vector Machines
Rong-En Fan, Pai-Hsuen Chen, Chih-Jen Lin; 6(Dec):1889--1918, 2005.
[abs][pdf]

New Horn Revision Algorithms
Judy Goldsmith, Robert H. Sloan; 6(Dec):1919--1938, 2005.
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A Unifying View of Sparse Approximate Gaussian Process Regression
Joaquin Quiñonero-Candela, Carl Edward Rasmussen; 6(Dec):1939--1959, 2005.
[abs][pdf]

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; 6(Dec):1961--1998, 2005.
[abs][pdf]

Change Point Problems in Linear Dynamical Systems
Onno Zoeter, Tom Heskes; 6(Dec):1999--2026, 2005.
[abs][pdf]

Asymptotics in Empirical Risk Minimization
Leila Mohammadi, Sara van de Geer; 6(Dec):2027--2047, 2005.
[abs][pdf]

Convergence Theorems for Generalized Alternating Minimization Procedures
Asela Gunawardana, William Byrne; 6(Dec):2049--2073, 2005.
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Kernel Methods for Measuring Independence
Arthur Gretton, Ralf Herbrich, Alexander Smola, Olivier Bousquet, Bernhard Schölkopf; 6(Dec):2075--2129, 2005.
[abs][pdf]

Efficient Margin Maximizing with Boosting
Gunnar Rätsch, Manfred K. Warmuth; 6(Dec):2131--2152, 2005.
[abs][pdf]

On the Nyström Method for Approximating a Gram Matrix for Improved Kernel-Based Learning
Petros Drineas, Michael W. Mahoney; 6(Dec):2153--2175, 2005.
[abs][pdf]

Expectation Consistent Approximate Inference
Manfred Opper, Ole Winther; 6(Dec):2177--2204, 2005.
[abs][pdf]




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