JMLR Volume 6
- Asymptotic Model Selection for Naive Bayesian Networks
- Dmitry Rusakov, Dan Geiger; (1):1−35, 2005.
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- Dimension Reduction in Text Classification with Support Vector Machines
- Hyunsoo Kim, Peg Howland, Haesun Park; (2):37−53, 2005.
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- Stability of Randomized Learning Algorithms
- Andre Elisseeff, Theodoros Evgeniou, Massimiliano Pontil; (3):55−79, 2005.
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- Learning Hidden Variable Networks: The Information Bottleneck Approach
- Gal Elidan, Nir Friedman; (4):81−127, 2005.
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- Diffusion Kernels on Statistical Manifolds
- John Lafferty, Guy Lebanon; (5):129−163, 2005.
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- Information Bottleneck for Gaussian Variables
- Gal Chechik, Amir Globerson, Naftali Tishby, Yair Weiss; (6):165−188, 2005.
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- Multiclass Boosting for Weak Classifiers
- Günther Eibl, Karl-Peter Pfeiffer; (7):189−210, 2005.
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- A Classification Framework for Anomaly Detection
- Ingo Steinwart, Don Hush, Clint Scovel; (8):211−232, 2005.
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- Denoising Source Separation
- Jaakko Särelä, Harri Valpola; (9):233−272, 2005.
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- Tutorial on Practical Prediction Theory for Classification
- John Langford; (10):273−306, 2005.
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- Generalization Bounds and Complexities Based on Sparsity and Clustering for Convex Combinations of Functions from Random Classes
- Savina Andonova Jaeger; (11):307−340, 2005.
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- A Modified Finite Newton Method for Fast Solution of Large Scale Linear SVMs
- S. Sathiya Keerthi, Dennis DeCoste; (12):341−361, 2005.
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- Core Vector Machines: Fast SVM Training on Very Large Data Sets
- Ivor W. Tsang, James T. Kwok, Pak-Ming Cheung; (13):363−392, 2005.
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- Generalization Bounds for the Area Under the ROC Curve
- Shivani Agarwal, Thore Graepel, Ralf Herbrich, Sariel Har-Peled, Dan Roth; (14):393−425, 2005.
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- Learning with Decision Lists of Data-Dependent Features
- Mario Marchand, Marina Sokolova; (15):427−451, 2005.
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- Estimating Functions for Blind Separation When Sources Have Variance Dependencies
- Motoaki Kawanabe, Klaus-Robert Müller; (16):453−482, 2005.
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- Characterization of a Family of Algorithms for Generalized Discriminant Analysis on Undersampled Problems
- Jieping Ye; (17):483−502, 2005.
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- Tree-Based Batch Mode Reinforcement Learning
- Damien Ernst, Pierre Geurts, Louis Wehenkel; (18):503−556, 2005.
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- Learning Module Networks
- Eran Segal, Dana Pe'er, Aviv Regev, Daphne Koller, Nir Friedman; (19):557−588, 2005.
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- 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.
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- Learning Multiple Tasks with Kernel Methods
- Theodoros Evgeniou, Charles A. Micchelli, Massimiliano Pontil; (21):615−637, 2005.
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- Adaptive Online Prediction by Following the Perturbed Leader
- Marcus Hutter, Jan Poland; (22):639−660, 2005.
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- Variational Message Passing
- John Winn, Christopher M. Bishop; (23):661−694, 2005.
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- Estimation of Non-Normalized Statistical Models by Score Matching
- Aapo Hyvärinen; (24):695−709, 2005.
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- Smooth ε-Insensitive Regression by Loss Symmetrization
- Ofer Dekel, Shai Shalev-Shwartz, Yoram Singer; (25):711−741, 2005.
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- Quasi-Geodesic Neural Learning Algorithms Over the Orthogonal Group: A Tutorial
- Simone Fiori; (26):743−781, 2005.
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- 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.
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- Multiclass Classification with Multi-Prototype Support Vector Machines
- Fabio Aiolli, Alessandro Sperduti; (28):817−850, 2005.
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- Prioritization Methods for Accelerating MDP Solvers
- David Wingate, Kevin D. Seppi; (29):851−881, 2005.
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- Learning from Examples as an Inverse Problem
- Ernesto De Vito, Lorenzo Rosasco, Andrea Caponnetto, Umberto De Giovannini, Francesca Odone; (30):883−904, 2005.
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- Loopy Belief Propagation: Convergence and Effects of Message Errors
- Alexander T. Ihler, John W. Fisher III, Alan S. Willsky; (31):905−936, 2005.
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- Learning a Mahalanobis Metric from Equivalence Constraints
- Aharon Bar-Hillel, Tomer Hertz, Noam Shental, Daphna Weinshall; (32):937−965, 2005.
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- Algorithmic Stability and Meta-Learning
- Andreas Maurer; (33):967−994, 2005.
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- Matrix Exponentiated Gradient Updates for On-line Learning and Bregman Projection
- Koji Tsuda, Gunnar Rätsch, Manfred K. Warmuth; (34):995−1018, 2005.
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- Gaussian Processes for Ordinal Regression
- Wei Chu, Zoubin Ghahramani; (35):1019−1041, 2005.
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- Learning the Kernel with Hyperkernels
- Cheng Soon Ong, Alexander J. Smola, Robert C. Williamson; (36):1043−1071, 2005.
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- A Generalization Error for Q-Learning
- Susan A. Murphy; (37):1073−1097, 2005.
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- Learning the Kernel Function via Regularization
- Charles A. Micchelli, Massimiliano Pontil; (38):1099−1125, 2005.
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- Analysis of Variance of Cross-Validation Estimators of the Generalization Error
- Marianthi Markatou, Hong Tian, Shameek Biswas, George Hripcsak; (39):1127−1168, 2005.
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- Semigroup Kernels on Measures
- Marco Cuturi, Kenji Fukumizu, Jean-Philippe Vert; (40):1169−1198, 2005.
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- Separating a Real-Life Nonlinear Image Mixture
- Luís B. Almeida; (41):1199−1229, 2005.
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- Concentration Bounds for Unigram Language Models
- Evgeny Drukh, Yishay Mansour; (42):1231−1264, 2005.
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- An MDP-Based Recommender System
- Guy Shani, David Heckerman, Ronen I. Brafman; (43):1265−1295, 2005.
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- Universal Algorithms for Learning Theory Part I : Piecewise Constant Functions
- Peter Binev, Albert Cohen, Wolfgang Dahmen, Ronald DeVore, Vladimir Temlyakov; (44):1297−1321, 2005.
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- Efficient Computation of Gapped Substring Kernels on Large Alphabets
- Juho Rousu, John Shawe-Taylor; (45):1323−1344, 2005.
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- Clustering on the Unit Hypersphere using von Mises-Fisher Distributions
- Arindam Banerjee, Inderjit S. Dhillon, Joydeep Ghosh, Suvrit Sra; (46):1345−1382, 2005.
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- Inner Product Spaces for Bayesian Networks
- Atsuyoshi Nakamura, Michael Schmitt, Niels Schmitt, Hans Ulrich Simon; (47):1383−1403, 2005.
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- Maximum Margin Algorithms with Boolean Kernels
- Roni Khardon, Rocco A. Servedio; (48):1405−1429, 2005.
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- A Bayes Optimal Approach for Partitioning the Values of Categorical Attributes
- Marc Boullé; (49):1431−1452, 2005.
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- Large Margin Methods for Structured and Interdependent Output Variables
- Ioannis Tsochantaridis, Thorsten Joachims, Thomas Hofmann, Yasemin Altun; (50):1453−1484, 2005.
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- Frames, Reproducing Kernels, Regularization and Learning
- Alain Rakotomamonjy, Stéphane Canu; (51):1485−1515, 2005.
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- Local Propagation in Conditional Gaussian Bayesian Networks
- Robert G. Cowell; (52):1517−1550, 2005.
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- A Bayesian Model for Supervised Clustering with the Dirichlet Process Prior
- Hal Daumé III, Daniel Marcu; (53):1551−1577, 2005.
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- Fast Kernel Classifiers with Online and Active Learning
- Antoine Bordes, Seyda Ertekin, Jason Weston, Léon Bottou; (54):1579−1619, 2005.
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- Managing Diversity in Regression Ensembles
- Gavin Brown, Jeremy L. Wyatt, Peter Tiňo; (55):1621−1650, 2005.
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- Active Coevolutionary Learning of Deterministic Finite Automata
- Josh Bongard, Hod Lipson; (56):1651−1678, 2005.
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- Assessing Approximate Inference for Binary Gaussian Process Classification
- Malte Kuss, Carl Edward Rasmussen; (57):1679−1704, 2005.
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- Clustering with Bregman Divergences
- Arindam Banerjee, Srujana Merugu, Inderjit S. Dhillon, Joydeep Ghosh; (58):1705−1749, 2005.
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- Combining Information Extraction Systems Using Voting and Stacked Generalization
- Georgios Sigletos, Georgios Paliouras, Constantine D. Spyropoulos, Michalis Hatzopoulos; (59):1751−1782, 2005.
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- Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models
- Neil Lawrence; (60):1783−1816, 2005.
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- A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data
- Rie Kubota Ando, Tong Zhang; (61):1817−1853, 2005.
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- Feature Selection for Unsupervised and Supervised Inference: The Emergence of Sparsity in a Weight-Based Approach
- Lior Wolf, Amnon Shashua; (62):1855−1887, 2005.
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- 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.
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- New Horn Revision Algorithms
- Judy Goldsmith, Robert H. Sloan; (64):1919−1938, 2005.
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- A Unifying View of Sparse Approximate Gaussian Process Regression
- Joaquin Quiñonero-Candela, Carl Edward Rasmussen; (65):1939−1959, 2005.
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- 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.
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- Change Point Problems in Linear Dynamical Systems
- Onno Zoeter, Tom Heskes; (67):1999−2026, 2005.
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- Asymptotics in Empirical Risk Minimization
- Leila Mohammadi, Sara van de Geer; (68):2027−2047, 2005.
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- Convergence Theorems for Generalized Alternating Minimization Procedures
- Asela Gunawardana, William Byrne; (69):2049−2073, 2005.
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- Kernel Methods for Measuring Independence
- Arthur Gretton, Ralf Herbrich, Alexander Smola, Olivier Bousquet, Bernhard Schölkopf; (70):2075−2129, 2005.
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- Efficient Margin Maximizing with Boosting
- Gunnar Rätsch, Manfred K. Warmuth; (71):2131−2152, 2005.
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- On the Nystrom Method for Approximating a Gram Matrix for Improved Kernel-Based Learning
- Petros Drineas, Michael W. Mahoney; (72):2153−2175, 2005.
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- Expectation Consistent Approximate Inference
- Manfred Opper, Ole Winther; (73):2177−2204, 2005.
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