JMLR Volume 6

• Asymptotic Model Selection for Naive Bayesian Networks

  Dmitry Rusakov, Dan Geiger (1):1−35, 2005 PDF BibTeX

• Dimension Reduction in Text Classification with Support Vector Machines

  Hyunsoo Kim, Peg Howland, Haesun Park (2):37−53, 2005 PDF BibTeX

• Stability of Randomized Learning Algorithms

  Andre Elisseeff, Theodoros Evgeniou, Massimiliano Pontil (3):55−79, 2005 PDF BibTeX

• Learning Hidden Variable Networks: The Information Bottleneck Approach

  Gal Elidan, Nir Friedman (4):81−127, 2005 PDF BibTeX

• Diffusion Kernels on Statistical Manifolds

  John Lafferty, Guy Lebanon (5):129−163, 2005 PDF BibTeX

• Information Bottleneck for Gaussian Variables

  Gal Chechik, Amir Globerson, Naftali Tishby, Yair Weiss (6):165−188, 2005 PDF BibTeX

• Multiclass Boosting for Weak Classifiers

  Günther Eibl, Karl-Peter Pfeiffer (7):189−210, 2005 PDF BibTeX

• A Classification Framework for Anomaly Detection

  Ingo Steinwart, Don Hush, Clint Scovel (8):211−232, 2005 PDF BibTeX

• Denoising Source Separation

  Jaakko Särelä, Harri Valpola (9):233−272, 2005 PDF BibTeX

• Tutorial on Practical Prediction Theory for Classification

  John Langford (10):273−306, 2005 PDF BibTeX

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

  Savina Andonova Jaeger (11):307−340, 2005 PDF BibTeX

• A Modified Finite Newton Method for Fast Solution of Large Scale Linear SVMs

  S. Sathiya Keerthi, Dennis DeCoste (12):341−361, 2005 PDF BibTeX

• Core Vector Machines: Fast SVM Training on Very Large Data Sets

  Ivor W. Tsang, James T. Kwok, Pak-Ming Cheung (13):363−392, 2005 PDF BibTeX

• Generalization Bounds for the Area Under the ROC Curve

  Shivani Agarwal, Thore Graepel, Ralf Herbrich, Sariel Har-Peled, Dan Roth (14):393−425, 2005 PDF BibTeX

• Learning with Decision Lists of Data-Dependent Features

  Mario Marchand, Marina Sokolova (15):427−451, 2005 PDF BibTeX

• Estimating Functions for Blind Separation When Sources Have Variance Dependencies

  Motoaki Kawanabe, Klaus-Robert Müller (16):453−482, 2005 PDF BibTeX

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

  Jieping Ye (17):483−502, 2005 PDF BibTeX

• Tree-Based Batch Mode Reinforcement Learning

  Damien Ernst, Pierre Geurts, Louis Wehenkel (18):503−556, 2005 PDF BibTeX

• Learning Module Networks

  Eran Segal, Dana Pe'er, Aviv Regev, Daphne Koller, Nir Friedman (19):557−588, 2005 PDF BibTeX

• Active Learning to Recognize Multiple Types of Plankton

  Tong Luo, Kurt Kramer, Dmitry B. Goldgof, Lawrence O. Hall, Scott Samson, Andrew Remsen, Thomas Hopkins (20):589−613, 2005 PDF BibTeX

• Learning Multiple Tasks with Kernel Methods

  Theodoros Evgeniou, Charles A. Micchelli, Massimiliano Pontil (21):615−637, 2005 PDF BibTeX

• Adaptive Online Prediction by Following the Perturbed Leader

  Marcus Hutter, Jan Poland (22):639−660, 2005 PDF BibTeX

• Variational Message Passing

  John Winn, Christopher M. Bishop (23):661−694, 2005 PDF BibTeX

• Estimation of Non-Normalized Statistical Models by Score Matching

  Aapo Hyvärinen (24):695−709, 2005 PDF BibTeX

• Smooth ε-Insensitive Regression by Loss Symmetrization

  Ofer Dekel, Shai Shalev-Shwartz, Yoram Singer (25):711−741, 2005 PDF BibTeX

• Quasi-Geodesic Neural Learning Algorithms Over the Orthogonal Group: A Tutorial

  Simone Fiori (26):743−781, 2005 PDF BibTeX

• Machine Learning Methods for Predicting Failures in Hard Drives: A Multiple-Instance Application

  Joseph F. Murray, Gordon F. Hughes, Kenneth Kreutz-Delgado (27):783−816, 2005 PDF BibTeX

• Multiclass Classification with Multi-Prototype Support Vector Machines

  Fabio Aiolli, Alessandro Sperduti (28):817−850, 2005 PDF BibTeX

• Prioritization Methods for Accelerating MDP Solvers

  David Wingate, Kevin D. Seppi (29):851−881, 2005 PDF BibTeX

• Learning from Examples as an Inverse Problem

  Ernesto De Vito, Lorenzo Rosasco, Andrea Caponnetto, Umberto De Giovannini, Francesca Odone (30):883−904, 2005 PDF BibTeX

• Loopy Belief Propagation: Convergence and Effects of Message Errors

  Alexander T. Ihler, John W. Fisher III, Alan S. Willsky (31):905−936, 2005 PDF BibTeX

• Learning a Mahalanobis Metric from Equivalence Constraints

  Aharon Bar-Hillel, Tomer Hertz, Noam Shental, Daphna Weinshall (32):937−965, 2005 PDF BibTeX

• Algorithmic Stability and Meta-Learning

  Andreas Maurer (33):967−994, 2005 PDF BibTeX

• Matrix Exponentiated Gradient Updates for On-line Learning and Bregman Projection

  Koji Tsuda, Gunnar Rätsch, Manfred K. Warmuth (34):995−1018, 2005 PDF BibTeX

• Gaussian Processes for Ordinal Regression

  Wei Chu, Zoubin Ghahramani (35):1019−1041, 2005 PDF BibTeX

• Learning the Kernel with Hyperkernels

  Cheng Soon Ong, Alexander J. Smola, Robert C. Williamson (36):1043−1071, 2005 PDF BibTeX

• A Generalization Error for Q-Learning

  Susan A. Murphy (37):1073−1097, 2005 PDF BibTeX

• Learning the Kernel Function via Regularization

  Charles A. Micchelli, Massimiliano Pontil (38):1099−1125, 2005 PDF BibTeX

• Analysis of Variance of Cross-Validation Estimators of the Generalization Error

  Marianthi Markatou, Hong Tian, Shameek Biswas, George Hripcsak (39):1127−1168, 2005 PDF BibTeX

• Semigroup Kernels on Measures

  Marco Cuturi, Kenji Fukumizu, Jean-Philippe Vert (40):1169−1198, 2005 PDF BibTeX

• Separating a Real-Life Nonlinear Image Mixture

  Luís B. Almeida (41):1199−1229, 2005 PDF BibTeX

• Concentration Bounds for Unigram Language Models

  Evgeny Drukh, Yishay Mansour (42):1231−1264, 2005 PDF BibTeX

• An MDP-Based Recommender System

  Guy Shani, David Heckerman, Ronen I. Brafman (43):1265−1295, 2005 PDF BibTeX

• Universal Algorithms for Learning Theory Part I : Piecewise Constant Functions

  Peter Binev, Albert Cohen, Wolfgang Dahmen, Ronald DeVore, Vladimir Temlyakov (44):1297−1321, 2005 PDF BibTeX

• Efficient Computation of Gapped Substring Kernels on Large Alphabets

  Juho Rousu, John Shawe-Taylor (45):1323−1344, 2005 PDF BibTeX

• Clustering on the Unit Hypersphere using von Mises-Fisher Distributions

  Arindam Banerjee, Inderjit S. Dhillon, Joydeep Ghosh, Suvrit Sra (46):1345−1382, 2005 PDF BibTeX

• Inner Product Spaces for Bayesian Networks

  Atsuyoshi Nakamura, Michael Schmitt, Niels Schmitt, Hans Ulrich Simon (47):1383−1403, 2005 PDF BibTeX

• Maximum Margin Algorithms with Boolean Kernels

  Roni Khardon, Rocco A. Servedio (48):1405−1429, 2005 PDF BibTeX

• A Bayes Optimal Approach for Partitioning the Values of Categorical Attributes

  Marc Boullé (49):1431−1452, 2005 PDF BibTeX

• Large Margin Methods for Structured and Interdependent Output Variables

  Ioannis Tsochantaridis, Thorsten Joachims, Thomas Hofmann, Yasemin Altun (50):1453−1484, 2005 PDF BibTeX

• Frames, Reproducing Kernels, Regularization and Learning

  Alain Rakotomamonjy, Stéphane Canu (51):1485−1515, 2005 PDF BibTeX

• Local Propagation in Conditional Gaussian Bayesian Networks

  Robert G. Cowell (52):1517−1550, 2005 PDF BibTeX

• A Bayesian Model for Supervised Clustering with the Dirichlet Process Prior

  Hal Daumé III, Daniel Marcu (53):1551−1577, 2005 PDF BibTeX

• Fast Kernel Classifiers with Online and Active Learning

  Antoine Bordes, Seyda Ertekin, Jason Weston, Léon Bottou (54):1579−1619, 2005 PDF BibTeX

• Managing Diversity in Regression Ensembles

  Gavin Brown, Jeremy L. Wyatt, Peter Tiňo (55):1621−1650, 2005 PDF BibTeX

• Active Coevolutionary Learning of Deterministic Finite Automata

  Josh Bongard, Hod Lipson (56):1651−1678, 2005 PDF BibTeX

• Assessing Approximate Inference for Binary Gaussian Process Classification

  Malte Kuss, Carl Edward Rasmussen (57):1679−1704, 2005 PDF BibTeX

• Clustering with Bregman Divergences

  Arindam Banerjee, Srujana Merugu, Inderjit S. Dhillon, Joydeep Ghosh (58):1705−1749, 2005 PDF BibTeX

• Combining Information Extraction Systems Using Voting and Stacked Generalization

  Georgios Sigletos, Georgios Paliouras, Constantine D. Spyropoulos, Michalis Hatzopoulos (59):1751−1782, 2005 PDF BibTeX

• Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models

  Neil Lawrence (60):1783−1816, 2005 PDF BibTeX

• A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data

  Rie Kubota Ando, Tong Zhang (61):1817−1853, 2005 PDF BibTeX

• Feature Selection for Unsupervised and Supervised Inference: The Emergence of Sparsity in a Weight-Based Approach

  Lior Wolf, Amnon Shashua (62):1855−1887, 2005 PDF BibTeX

• Working Set Selection Using Second Order Information for Training Support Vector Machines

  Rong-En Fan, Pai-Hsuen Chen, Chih-Jen Lin (63):1889−1918, 2005 PDF BibTeX

• New Horn Revision Algorithms

  Judy Goldsmith, Robert H. Sloan (64):1919−1938, 2005 PDF BibTeX

• A Unifying View of Sparse Approximate Gaussian Process Regression

  Joaquin Quiñonero-Candela, Carl Edward Rasmussen (65):1939−1959, 2005 PDF BibTeX

• 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 (66):1961−1998, 2005 PDF BibTeX

• Change Point Problems in Linear Dynamical Systems

  Onno Zoeter, Tom Heskes (67):1999−2026, 2005 PDF BibTeX

• Asymptotics in Empirical Risk Minimization

  Leila Mohammadi, Sara van de Geer (68):2027−2047, 2005 PDF BibTeX

• Convergence Theorems for Generalized Alternating Minimization Procedures

  Asela Gunawardana, William Byrne (69):2049−2073, 2005 PDF BibTeX

• Kernel Methods for Measuring Independence

  Arthur Gretton, Ralf Herbrich, Alexander Smola, Olivier Bousquet, Bernhard Schölkopf (70):2075−2129, 2005 PDF BibTeX

• Efficient Margin Maximizing with Boosting

  Gunnar Rätsch, Manfred K. Warmuth (71):2131−2152, 2005 PDF BibTeX

• On the Nystrom Method for Approximating a Gram Matrix for Improved Kernel-Based Learning

  Petros Drineas, Michael W. Mahoney (72):2153−2175, 2005 PDF BibTeX

• Expectation Consistent Approximate Inference

  Manfred Opper, Ole Winther (73):2177−2204, 2005 PDF BibTeX