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
Volume 6, March 2005
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, Dan Roth:
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 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
Volume 6, July 2005
Volume 6, August 2005
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 Tino:
Managing Diversity in Regression Ensembles.
1621-1650

Volume 6, October 2005
Volume 6, November 2005
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 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, Michael W. Mahoney:
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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