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Journal of Machine Learning Research, Volume 6
Volume 6, January 2005
- Dmitry Rusakov, Dan Geiger:
Asymptotic Model Selection for Naive Bayesian Networks. 1-35 - Hyunsoo Kim, Peg Howland, Haesun Park:
Dimension Reduction in Text Classification with Support Vector Machines. 37-53 - André Elisseeff, Theodoros Evgeniou, Massimiliano Pontil:
Stability of Randomized Learning Algorithms. 55-79 - Gal Elidan, Nir Friedman:
Learning Hidden Variable Networks: The Information Bottleneck Approach. 81-127 - John D. Lafferty, Guy Lebanon:
Diffusion Kernels on Statistical Manifolds. 129-163 - Gal Chechik, Amir Globerson, Naftali Tishby, Yair Weiss:
Information Bottleneck for Gaussian Variables. 165-188
Volume 6, February 2005
- Günther Eibl, Karl Peter Pfeiffer:
Multiclass Boosting for Weak Classifiers. 189-210 - Ingo Steinwart, Don R. Hush, Clint Scovel:
A Classification Framework for Anomaly Detection. 211-232
Volume 6, March 2005
- Jaakko Särelä, Harri Valpola:
Denoising Source Separation. 233-272 - John Langford:
Tutorial on Practical Prediction Theory for Classification. 273-306 - Savina Andonova Jaeger:
Generalization Bounds and Complexities Based on Sparsity and Clustering for Convex Combinations of Functions from Random Classes. 307-340 - S. Sathiya Keerthi, Dennis DeCoste:
A Modified Finite Newton Method for Fast Solution of Large Scale Linear SVMs. 341-361
Volume 6, April 2005
- Ivor W. Tsang
, James T. Kwok, Pak-Ming Cheung:
Core Vector Machines: Fast SVM Training on Very Large Data Sets. 363-392 - Shivani Agarwal, Thore Graepel, Ralf Herbrich, Sariel Har-Peled
Generalization Bounds for the Area Under the ROC Curve. 393-425 - Mario Marchand, Marina Sokolova:
Learning with Decision Lists of Data-Dependent Features. 427-451 - Motoaki Kawanabe, Klaus-Robert Müller:
Estimating Functions for Blind Separation When Sources Have Variance Dependencies. 453-482 - Jieping Ye:
Characterization of a Family of Algorithms for Generalized Discriminant Analysis on Undersampled Problems. 483-502 - Damien Ernst, Pierre Geurts, Louis Wehenkel:
Tree-Based Batch Mode Reinforcement Learning. 503-556 - Eran Segal, Dana Pe'er, Aviv Regev, Daphne Koller, Nir Friedman:
Learning Module Networks. 557-588 - Tong Luo, Kurt Kramer, Dmitry B. Goldgof, Lawrence O. Hall, Scott Samson, Andrew Remsen, Thomas Hopkins:
Active Learning to Recognize Multiple Types of Plankton. 589-613 - Theodoros Evgeniou, Charles A. Micchelli, Massimiliano Pontil:
Learning Multiple Tasks with Kernel Methods. 615-637 - Marcus Hutter, Jan Poland:
Adaptive Online Prediction by Following the Perturbed Leader. 639-660 - John M. Winn, Christopher M. Bishop:
Variational Message Passing. 661-694 - Aapo Hyvärinen:
Estimation of Non-Normalized Statistical Models by Score Matching. 695-709
Volume 6, May 2005
- Ofer Dekel, Shai Shalev-Shwartz, Yoram Singer:
Smooth epsiloon-Insensitive Regression by Loss Symmetrization. 711-741 - Simone G. O. Fiori:
Quasi-Geodesic Neural Learning Algorithms Over the Orthogonal Group: A Tutorial. 743-781 - Joseph F. Murray, Gordon F. Hughes, Kenneth Kreutz-Delgado:
Machine Learning Methods for Predicting Failures in Hard Drives: A Multiple-Instance Application. 783-816 - Fabio Aiolli, Alessandro Sperduti:
Multiclass Classification with Multi-Prototype Support Vector Machines. 817-850 - David Wingate, Kevin D. Seppi:
Prioritization Methods for Accelerating MDP Solvers. 851-881 - Ernesto De Vito, Lorenzo Rosasco, Andrea Caponnetto, Umberto De Giovannini, Francesca Odone:
Learning from Examples as an Inverse Problem. 883-904 - Alexander T. Ihler, John W. Fisher III, Alan S. Willsky:
Loopy Belief Propagation: Convergence and Effects of Message Errors. 905-936
Volume 6, June 2005
- Aharon Bar-Hillel, Tomer Hertz, Noam Shental, Daphna Weinshall:
Learning a Mahalanobis Metric from Equivalence Constraints. 937-965 - Andreas Maurer:
Algorithmic Stability and Meta-Learning. 967-994 - Koji Tsuda, Gunnar Rätsch, Manfred K. Warmuth:
Matrix Exponentiated Gradient Updates for On-line Learning and Bregman Projection. 995-1018
Volume 6, July 2005
- Wei Chu, Zoubin Ghahramani:
Gaussian Processes for Ordinal Regression. 1019-1041 - Cheng Soon Ong, Alexander J. Smola, Robert C. Williamson:
Learning the Kernel with Hyperkernels. 1043-1071 - Susan A. Murphy:
A Generalization Error for Q-Learning. 1073-1097 - Charles A. Micchelli, Massimiliano Pontil:
Learning the Kernel Function via Regularization. 1099-1125 - Marianthi Markatou, Hong Tian, Shameek Biswas, George Hripcsak:
Analysis of Variance of Cross-Validation Estimators of the Generalization Error. 1127-1168 - Marco Cuturi, Kenji Fukumizu, Jean-Philippe Vert:
Semigroup Kernels on Measures. 1169-1198 - Luís B. Almeida:
Separating a Real-Life Nonlinear Image Mixture. 1199-1229
Volume 6, August 2005
- Evgeny Drukh, Yishay Mansour:
Concentration Bounds for Unigram Language Models. 1231-1264
Volume 6, September 2005
- Guy Shani, David Heckerman, Ronen I. Brafman:
An MDP-Based Recommender System. 1265-1295 - Peter Binev, Albert Cohen, Wolfgang Dahmen, Ronald A. DeVore, Vladimir N. Temlyakov:
Universal Algorithms for Learning Theory Part I : Piecewise Constant Functions. 1297-1321 - Juho Rousu, John Shawe-Taylor:
Efficient Computation of Gapped Substring Kernels on Large Alphabets. 1323-1344 - Arindam Banerjee, Inderjit S. Dhillon, Joydeep Ghosh, Suvrit Sra:
Clustering on the Unit Hypersphere using von Mises-Fisher Distributions. 1345-1382 - Atsuyoshi Nakamura, Michael Schmitt, Niels Schmitt, Hans Ulrich Simon:
Inner Product Spaces for Bayesian Networks. 1383-1403 - Roni Khardon, Rocco A. Servedio:
Maximum Margin Algorithms with Boolean Kernels. 1405-1429 - Marc Boullé:
A Bayes Optimal Approach for Partitioning the Values of Categorical Attributes. 1431-1452 - Ioannis Tsochantaridis, Thorsten Joachims, Thomas Hofmann, Yasemin Altun:
Large Margin Methods for Structured and Interdependent Output Variables. 1453-1484 - Alain Rakotomamonjy, Stéphane Canu:
Frames, Reproducing Kernels, Regularization and Learning. 1485-1515 - Robert G. Cowell:
Local Propagation in Conditional Gaussian Bayesian Networks. 1517-1550 - Hal Daumé III, Daniel Marcu:
A Bayesian Model for Supervised Clustering with the Dirichlet Process Prior. 1551-1577 - Antoine Bordes, Seyda Ertekin, Jason Weston, Léon Bottou:
Fast Kernel Classifiers with Online and Active Learning. 1579-1619 - Gavin Brown, Jeremy L. Wyatt, Peter Tiño:
Managing Diversity in Regression Ensembles. 1621-1650
Volume 6, October 2005
- Josh C. Bongard, Hod Lipson:
Active Coevolutionary Learning of Deterministic Finite Automata. 1651-1678 - Malte Kuss, Carl Edward Rasmussen:
Assessing Approximate Inference for Binary Gaussian Process Classification. 1679-1704 - Arindam Banerjee, Srujana Merugu, Inderjit S. Dhillon, Joydeep Ghosh:
Clustering with Bregman Divergences. 1705-1749
Volume 6, November 2005
- Georgios Sigletos, Georgios Paliouras, Constantine D. Spyropoulos, Michael Hatzopoulos:
Combining Information Extraction Systems Using Voting and Stacked Generalization. 1751-1782 - Neil D. Lawrence:
Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models. 1783-1816 - Rie Kubota Ando, Tong Zhang:
A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data. 1817-1853 - Lior Wolf, Amnon Shashua:
Feature Selection for Unsupervised and Supervised Inference: The Emergence of Sparsity in a Weight-Based Approach. 1855-1887
Volume 6, December 2005
- Rong-En Fan, Pai-Hsuen Chen, Chih-Jen Lin:
Working Set Selection Using Second Order Information for Training Support Vector Machines. 1889-1918 - Judy Goldsmith, Robert H. Sloan:
New Horn Revision Algorithms. 1919-1938 - Joaquin Quiñonero Candela, Carl Edward Rasmussen:
A Unifying View of Sparse Approximate Gaussian Process Regression. 1939-1959 - Weng-Keen Wong, Andrew W. Moore, Gregory F. Cooper, Michael M. Wagner:
What's Strange About Recent Events (WSARE): An Algorithm for the Early Detection of Disease Outbreaks. 1961-1998 - Onno Zoeter, Tom Heskes:
Change Point Problems in Linear Dynamical Systems. 1999-2026 - Leila Mohammadi, Sara A. van de Geer:
Asymptotics in Empirical Risk Minimization. 2027-2047 - Asela Gunawardana, William Byrne:
Convergence Theorems for Generalized Alternating Minimization Procedures. 2049-2073 - Arthur Gretton, Ralf Herbrich, Alexander J. Smola, Olivier Bousquet, Bernhard Schölkopf:
Kernel Methods for Measuring Independence. 2075-2129 - Gunnar Rätsch, Manfred K. Warmuth:
Efficient Margin Maximizing with Boosting. 2131-2152 - Petros Drineas
On the Nyström Method for Approximating a Gram Matrix for Improved Kernel-Based Learning. 2153-2175 - Manfred Opper, Ole Winther:
Expectation Consistent Approximate Inference. 2177-2204
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