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, 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

• 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
  
• Luis 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 Tino:
  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 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
  

Last update Tue May 21 19:17:14 2013 CET by the DBLP Team — Data released under the ODC-BY 1.0 license — See also our legal information page