Journal of Machine Learning Research, Volume 7

Volume 7, January 2006

• Janez Demsar:
  Statistical Comparisons of Classifiers over Multiple Data Sets. 1-30
  
• Nicolò Cesa-Bianchi, Claudio Gentile, Luca Zaniboni:
  Incremental Algorithms for Hierarchical Classification. 31-54
  
• Michael Schmitt, Laura Martignon:
  On the Complexity of Learning Lexicographic Strategies. 55-83
  
• Tzu-Kuo Huang, Ruby C. Weng, Chih-Jen Lin:
  Generalized Bradley-Terry Models and Multi-Class Probability Estimates. 85-115
  
• Andreas Maurer:
  Bounds for Linear Multi-Task Learning. 117-139
  
• Masashi Sugiyama:
  Active Learning in Approximately Linear Regression Based on Conditional Expectation of Generalization Error. 141-166
  

Volume 7, February 2006

• Dana Pe'er, Amos Tanay, Aviv Regev:
  MinReg: A Scalable Algorithm for Learning Parsimonious Regulatory Networks in Yeast and Mammals. 167-189
  
• Ricardo Silva, Richard Scheines, Clark Glymour, Peter Spirtes:
  Learning the Structure of Linear Latent Variable Models. 191-246
  
• Gilles Blanchard, Motoaki Kawanabe, Masashi Sugiyama, Vladimir Spokoiny, Klaus-Robert Müller:
  In Search of Non-Gaussian Components of a High-Dimensional Distribution. 247-282
  
• Paul W. Goldberg:
  Some Discriminant-Based PAC Algorithms. 283-306
  
• Andrea Passerini, Paolo Frasconi, Luc De Raedt:
  Kernels on Prolog Proof Trees: Statistical Learning in the ILP Setting. 307-342
  
• Greg Hamerly, Erez Perelman, Jeremy Lau, Brad Calder, Timothy Sherwood:
  Using Machine Learning to Guide Architecture Simulation. 343-378
  
• Ron Begleiter, Ran El-Yaniv:
  Superior Guarantees for Sequential Prediction and Lossless Compression via Alphabet Decomposition. 379-411
  
• Rémi Munos:
  Geometric Variance Reduction in Markov Chains: Application to Value Function and Gradient Estimation. 413-427
  
• Emanuel Kitzelmann, Ute Schmid:
  Inductive Synthesis of Functional Programs: An Explanation Based Generalization Approach. 429-454
  
• Tonatiuh Peña Centeno, Neil D. Lawrence:
  Optimising Kernel Parameters and Regularisation Coefficients for Non-linear Discriminant Analysis. 455-491
  

Volume 7, March 2006

• Pat Langley, Dongkyu Choi:
  Learning Recursive Control Programs from Problem Solving. 493-518
  
• Sayan Mukherjee, Ding-Xuan Zhou:
  Learning Coordinate Covariances via Gradients. 519-549
  
• Koby Crammer, Ofer Dekel, Joseph Keshet, Shai Shalev-Shwartz, Yoram Singer:
  Online Passive-Aggressive Algorithms. 551-585
  

Volume 7, April 2006

• Adam R. Klivans, Rocco A. Servedio:
  Toward Attribute Efficient Learning of Decision Lists and Parities. 587-602
  
• Mingrui Wu, Bernhard Schölkopf, Gökhan H. Bakir:
  A Direct Method for Building Sparse Kernel Learning Algorithms. 603-624
  
• Kazuho Watanabe, Sumio Watanabe:
  Stochastic Complexities of Gaussian Mixtures in Variational Bayesian Approximation. 625-644
  
• Daniil Ryabko:
  Pattern Recognition for Conditionally Independent Data. 645-664
  
• Clayton Scott, Robert D. Nowak:
  Learning Minimum Volume Sets. 665-704
  

Volume 7, May 2006

• Peter J. Bickel, Yaacov Ritov, Alon Zakai:
  Some Theory for Generalized Boosting Algorithms. 705-732
  
• Don R. Hush, Patrick Kelly, Clint Scovel, Ingo Steinwart:
  QP Algorithms with Guaranteed Accuracy and Run Time for Support Vector Machines. 733-769
  
• Rémi Munos:
  Policy Gradient in Continuous Time. 771-791
  
• Michael W. Spratling:
  Learning Image Components for Object Recognition. 793-815
  
• Régis Vert, Jean-Philippe Vert:
  Consistency and Convergence Rates of One-Class SVMs and Related Algorithms. 817-854
  
• Ross A. Lippert, Ryan M. Rifkin:
  Infinite-sigma Limits For Tikhonov Regularization. 855-876
  
• Shimon Whiteson, Peter Stone:
  Evolutionary Function Approximation for Reinforcement Learning. 877-917
  

Volume 7, June 2006

• Sharlee Climer, Weixiong Zhang:
  Rearrangement Clustering: Pitfalls, Remedies, and Applications. 919-943
  
• Seyoung Kim, Padhraic Smyth:
  Segmental Hidden Markov Models with Random Effects for Waveform Modeling. 945-969
  
• Guillaume Lecué:
  Lower Bounds and Aggregation in Density Estimation. 971-981
  
• Nicolai Meinshausen:
  Quantile Regression Forests. 983-999
  
• Peter Bühlmann, Bin Yu:
  Sparse Boosting. 1001-1024
  
• Andrew B. Gardner, Abba M. Krieger, George J. Vachtsevanos, Brian Litt:
  One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. 1025-1044
  
• Alberto Roverato, Milan Studený:
  A Graphical Representation of Equivalence Classes of AMP Chain Graphs. 1045-1078
  
• Eyal Even-Dar, Shie Mannor, Yishay Mansour:
  Action Elimination and Stopping Conditions for the Multi-Armed Bandit and Reinforcement Learning Problems. 1079-1105
  
• S. V. N. Vishwanathan, Nicol N. Schraudolph, Alex J. Smola:
  Step Size Adaptation in Reproducing Kernel Hilbert Space. 1107-1133
  
• Ting Liu, Andrew W. Moore, Alexander G. Gray:
  New Algorithms for Efficient High-Dimensional Nonparametric Classification. 1135-1158
  

Volume 7, July 2006

• Enrique F. Castillo, Bertha Guijarro-Berdiñas, Oscar Fontenla-Romero, Amparo Alonso-Betanzos:
  A Very Fast Learning Method for Neural Networks Based on Sensitivity Analysis. 1159-1182
  
• Jieping Ye, Tao Xiong:
  Computational and Theoretical Analysis of Null Space and Orthogonal Linear Discriminant Analysis. 1183-1204
  
• Nicolò Cesa-Bianchi, Claudio Gentile, Luca Zaniboni:
  Worst-Case Analysis of Selective Sampling for Linear Classification. 1205-1230
  
• Ichiro Takeuchi, Quoc V. Le, Tim D. Sears, Alexander J. Smola:
  Nonparametric Quantile Estimation. 1231-1264
  

• Kristin P. Bennett, Emilio Parrado-Hernández:
  The Interplay of Optimization and Machine Learning Research. 1265-1281
  
• Pannagadatta K. Shivaswamy, Chiranjib Bhattacharyya, Alexander J. Smola:
  Second Order Cone Programming Approaches for Handling Missing and Uncertain Data. 1283-1314
  
• Yi Zhang, Samuel Burer, W. Nick Street:
  Ensemble Pruning Via Semi-definite Programming. 1315-1338
  
• Anders Bergkvist, Peter Damaschke, Marcel Lüthi:
  Linear Programs for Hypotheses Selection in Probabilistic Inference Models. 1339-1355
  
• Radu Stefan Niculescu, Tom M. Mitchell, R. Bharat Rao:
  Bayesian Network Learning with Parameter Constraints. 1357-1383
  
• Matthias Heiler, Christoph Schnörr:
  Learning Sparse Representations by Non-Negative Matrix Factorization and Sequential Cone Programming. 1385-1407
  
• Tijl De Bie, Nello Cristianini:
  Fast SDP Relaxations of Graph Cut Clustering, Transduction, and Other Combinatorial Problem. 1409-1436
  
• Tobias Glasmachers, Christian Igel:
  Maximum-Gain Working Set Selection for SVMs. 1437-1466
  
• Luca Zanni, Thomas Serafini, Gaetano Zanghirati:
  Parallel Software for Training Large Scale Support Vector Machines on Multiprocessor Systems. 1467-1492
  
• S. Sathiya Keerthi, Olivier Chapelle, Dennis DeCoste:
  Building Support Vector Machines with Reduced Classifier Complexity. 1493-1515
  
• Olvi L. Mangasarian:
  Exact 1-Norm Support Vector Machines Via Unconstrained Convex Differentiable Minimization. 1517-1530
  
• Sören Sonnenburg, Gunnar Rätsch, Christin Schäfer, Bernhard Schölkopf:
  Large Scale Multiple Kernel Learning. 1531-1565
  
• Shai Shalev-Shwartz, Yoram Singer:
  Efficient Learning of Label Ranking by Soft Projections onto Polyhedra. 1567-1599
  
• Juho Rousu, Craig Saunders, Sándor Szedmák, John Shawe-Taylor:
  Kernel-Based Learning of Hierarchical Multilabel Classification Models. 1601-1626
  
• Benjamin Taskar, Simon Lacoste-Julien, Michael I. Jordan:
  Structured Prediction, Dual Extragradient and Bregman Projections. 1627-1653
  

Volume 7, August 2006

• Hema Raghavan, Omid Madani, Rosie Jones:
  Active Learning with Feedback on Features and Instances. 1655-1686
  
• Ronan Collobert, Fabian H. Sinz, Jason Weston, Léon Bottou:
  Large Scale Transductive SVMs. 1687-1712
  
• Francis R. Bach, David Heckerman, Eric Horvitz:
  Considering Cost Asymmetry in Learning Classifiers. 1713-1741
  
• Pieter Abbeel, Daphne Koller, Andrew Y. Ng:
  Learning Factor Graphs in Polynomial Time and Sample Complexity. 1743-1788
  

Volume 7, September 2006

• Jelle R. Kok, Nikos A. Vlassis:
  Collaborative Multiagent Reinforcement Learning by Payoff Propagation. 1789-1828
  
• Martin J. Wainwright:
  Estimating the "Wrong" Graphical Model: Benefits in the Computation-Limited Setting. 1829-1859
  
• Jing Zhou, Dean P. Foster, Robert A. Stine, Lyle H. Ungar:
  Streamwise Feature Selection. 1861-1885
  

• Chen Yanover, Talya Meltzer, Yair Weiss:
  Linear Programming Relaxations and Belief Propagation - An Empirical Study. 1887-1907
  
• Pavel Laskov, Christian Gehl, Stefan Krüger, Klaus-Robert Müller:
  Incremental Support Vector Learning: Analysis, Implementation and Applications. 1909-1936
  

Volume 7, October 2006

• Shalabh Bhatnagar, Vivek S. Borkar, Madhukar Akarapu:
  A Simulation-Based Algorithm for Ergodic Control of Markov Chains Conditioned on Rare Events. 1937-1962
  
• Francis R. Bach, Michael I. Jordan:
  Learning Spectral Clustering, With Application To Speech Separation. 1963-2001
  
• Shohei Shimizu, Patrik O. Hoyer, Aapo Hyvärinen, Antti J. Kerminen:
  A Linear Non-Gaussian Acyclic Model for Causal Discovery. 2003-2030
  
• Dmitry M. Malioutov, Jason K. Johnson, Alan S. Willsky:
  Walk-Sums and Belief Propagation in Gaussian Graphical Models. 2031-2064
  
• Thomas Kämpke:
  Distance Patterns in Structural Similarity. 2065-2086
  
• Hichem Sahbi, Donald Geman:
  A Hierarchy of Support Vector Machines for Pattern Detection. 2087-2123
  
• Fu Chang, Chin-Chin Lin, Chi-Jen Lu:
  Adaptive Prototype Learning Algorithms: Theoretical and Experimental Studies. 2125-2148
  
• Luis M. de Campos:
  A Scoring Function for Learning Bayesian Networks based on Mutual Information and Conditional Independence Tests. 2149-2187
  
• Tomás Singliar, Milos Hauskrecht:
  Noisy-OR Component Analysis and its Application to Link Analysis. 2189-2213
  
• Dana Angluin, Jiang Chen:
  Learning a Hidden Hypergraph. 2215-2236
  

• Katya Scheinberg:
  An Efficient Implementation of an Active Set Method for SVMs. 2237-2257
  

Volume 7, November 2006

• Anders Jonsson, Andrew G. Barto:
  Causal Graph Based Decomposition of Factored MDPs. 2259-2301
  
• Mikio L. Braun:
  Accurate Error Bounds for the Eigenvalues of the Kernel Matrix. 2303-2328
  
• Josep M. Porta, Nikos A. Vlassis, Matthijs T. J. Spaan, Pascal Poupart:
  Point-Based Value Iteration for Continuous POMDPs. 2329-2367
  
• David A. Ross, Richard S. Zemel:
  Learning Parts-Based Representations of Data. 2369-2397
  
• Mikhail Belkin, Partha Niyogi, Vikas Sindhwani:
  Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples. 2399-2434
  
• Di-Rong Chen, Tao Sun:
  Consistency of Multiclass Empirical Risk Minimization Methods Based on Convex Loss. 2435-2447
  
• Magnus Ekdahl, Timo Koski:
  Bounds for the Loss in Probability of Correct Classification Under Model Based Approximation. 2449-2480
  
• Sayan Mukherjee, Qiang Wu:
  Estimation of Gradients and Coordinate Covariation in Classification. 2481-2514
  
• David Barber:
  Expectation Correction for Smoothed Inference in Switching Linear Dynamical Systems. 2515-2540
  
• Peng Zhao, Bin Yu:
  On Model Selection Consistency of Lasso. 2541-2563
  

Volume 6, December 2006

• Andrea Caponnetto, Alexander Rakhlin:
  Stability Properties of Empirical Risk Minimization over Donsker Classes. 2565-2583
  
• Rasmus Kongsgaard Olsson, Lars Kai Hansen:
  Linear State-Space Models for Blind Source Separation. 2585-2602
  
• Bernhard Moser:
  On Representing and Generating Kernels by Fuzzy Equivalence Relations. 2603-2620
  
• Robert Castelo, Alberto Roverato:
  A Robust Procedure For Gaussian Graphical Model Search From Microarray Data With p Larger Than n. 2621-2650
  
• Charles A. Micchelli, Yuesheng Xu, Haizhang Zhang:
  Universal Kernels. 2651-2667
  

• Philip K. Chan, Richard Lippmann:
  Machine Learning for Computer Security. 2669-2672
  
• Andrej Bratko, Gordon V. Cormack, Bogdan Filipic, Thomas R. Lynam, Blaz Zupan:
  Spam Filtering Using Statistical Data Compression Models. 2673-2698
  
• Giorgio Fumera, Ignazio Pillai, Fabio Roli:
  Spam Filtering Based On The Analysis Of Text Information Embedded Into Images. 2699-2720
  
• Jeremy Z. Kolter, Marcus A. Maloof:
  Learning to Detect and Classify Malicious Executables in the Wild. 2721-2744
  
• Charles V. Wright, Fabian Monrose, Gerald M. Masson:
  On Inferring Application Protocol Behaviors in Encrypted Network Traffic. 2745-2769
  

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