Journal of Machine Learning Research, Volume 6
Volume 6, January 2005
- 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
- Gal Chechik, Amir Globerson, Naftali Tishby, Yair Weiss: Information Bottleneck for Gaussian Variables. 165-188
Volume 6, February 2005
- Ingo Steinwart, Don R. Hush, Clint Scovel: A Classification Framework for Anomaly Detection. 211-232
Volume 6, March 2005
- 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
- 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
- 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
- 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
- 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
- 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
- Luis B. Almeida: Separating a Real-Life Nonlinear Image Mixture. 1199-1229
Volume 6, August 2005
Volume 6, September 2005
- 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
- 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
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
- 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
- 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
- Petros Drineas, Michael W. Mahoney: On the Nyström Method for Approximating a Gram Matrix for Improved Kernel-Based Learning. 2153-2175
data released under the ODC-BY 1.0 license. See also our legal information page
last updated on 2013-06-19T201401 by the dblp team