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JMLR Volume 7

Statistical Comparisons of Classifiers over Multiple Data Sets
Janez Demšar; (1):1−30, 2006.
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Incremental Algorithms for Hierarchical Classification
Nicoló Cesa-Bianchi, Claudio Gentile, Luca Zaniboni; (2):31−54, 2006.
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On the Complexity of Learning Lexicographic Strategies
Michael Schmitt, Laura Martignon; (3):55−83, 2006.
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Generalized Bradley-Terry Models and Multi-Class Probability Estimates
Tzu-Kuo Huang, Ruby C. Weng, Chih-Jen Lin; (4):85−115, 2006.
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Bounds for Linear Multi-Task Learning
Andreas Maurer; (5):117−139, 2006.
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Active Learning in Approximately Linear Regression Based on Conditional Expectation of Generalization Error
Masashi Sugiyama; (6):141−166, 2006.
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MinReg: A Scalable Algorithm for Learning Parsimonious Regulatory Networks in Yeast and Mammals
Dana Pe'er, Amos Tanay, Aviv Regev; (7):167−189, 2006.
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Learning the Structure of Linear Latent Variable Models
Ricardo Silva, Richard Scheine, Clark Glymour, Peter Spirtes; (8):191−246, 2006.
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In Search of Non-Gaussian Components of a High-Dimensional Distribution
Gilles Blanchard, Motoaki Kawanabe, Masashi Sugiyama, Vladimir Spokoiny, Klaus-Robert Müller; (9):247−282, 2006.
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Some Discriminant-Based PAC Algorithms
Paul W. Goldberg; (10):283−306, 2006.
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Kernels on Prolog Proof Trees: Statistical Learning in the ILP Setting
Andrea Passerini, Paolo Frasconi, Luc De Raedt; (11):307−342, 2006.
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Using Machine Learning to Guide Architecture Simulation
Greg Hamerly, Erez Perelman, Jeremy Lau, Brad Calder, Timothy Sherwood; (12):343−378, 2006.
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Superior Guarantees for Sequential Prediction and Lossless Compression via Alphabet Decomposition
Ron Begleiter, Ran El-Yaniv; (13):379−411, 2006.
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Geometric Variance Reduction in Markov Chains: Application to Value Function and Gradient Estimation
Rémi Munos; (14):413−427, 2006.
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Inductive Synthesis of Functional Programs: An Explanation Based Generalization Approach
Emanuel Kitzelmann, Ute Schmid; (15):429−454, 2006.
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Optimising Kernel Parameters and Regularisation Coefficients for Non-linear Discriminant Analysis
Tonatiuh Peña Centeno, Neil D. Lawrence; (16):455−491, 2006.
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Learning Recursive Control Programs from Problem Solving
Pat Langley, Dongkyu Choi; (17):493−518, 2006.
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Learning Coordinate Covariances via Gradients
Sayan Mukherjee, Ding-Xuan Zhou; (18):519−549, 2006.
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Online Passive-Aggressive Algorithms
Koby Crammer, Ofer Dekel, Joseph Keshet, Shai Shalev-Shwartz, Yoram Singer; (19):551−585, 2006.
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Toward Attribute Efficient Learning of Decision Lists and Parities
Adam R. Klivans, Rocco A. Servedio; (20):587−602, 2006.
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A Direct Method for Building Sparse Kernel Learning Algorithms
Mingrui Wu, Bernhard Schölkopf, Gökhan Bakir; (21):603−624, 2006.
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Stochastic Complexities of Gaussian Mixtures in Variational Bayesian Approximation
Kazuho Watanabe, Sumio Watanabe; (22):625−644, 2006.
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Pattern Recognition for Conditionally Independent Data
Daniil Ryabko; (23):645−664, 2006.
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Learning Minimum Volume Sets
Clayton D. Scott, Robert D. Nowak; (24):665−704, 2006.
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Some Theory for Generalized Boosting Algorithms
Peter J. Bickel, Ya'acov Ritov, Alon Zakai; (25):705−732, 2006.
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QP Algorithms with Guaranteed Accuracy and Run Time for Support Vector Machines
Don Hush, Patrick Kelly, Clint Scovel, Ingo Steinwart; (26):733−769, 2006.
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Policy Gradient in Continuous Time
Rémi Munos; (27):771−791, 2006.
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Learning Image Components for Object Recognition
Michael W. Spratling; (28):793−815, 2006.
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Consistency and Convergence Rates of One-Class SVMs and Related Algorithms
Régis Vert, Jean-Philippe Vert; (29):817−854, 2006.
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Infinite-σ Limits For Tikhonov Regularization
Ross A. Lippert, Ryan M. Rifkin; (30):855−876, 2006.
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Evolutionary Function Approximation for Reinforcement Learning
Shimon Whiteson, Peter Stone; (31):877−917, 2006.
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Rearrangement Clustering: Pitfalls, Remedies, and Applications
Sharlee Climer, Weixiong Zhang; (32):919−943, 2006.
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Segmental Hidden Markov Models with Random Effects for Waveform Modeling
Seyoung Kim, Padhraic Smyth; (33):945−969, 2006.
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Lower Bounds and Aggregation in Density Estimation
Guillaume Lecué; (34):971−981, 2006.
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Quantile Regression Forests
Nicolai Meinshausen; (35):983−999, 2006.
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Sparse Boosting
Peter Bühlmann, Bin Yu; (36):1001−1024, 2006.
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One-Class Novelty Detection for Seizure Analysis from Intracranial EEG
Andrew B. Gardner, Abba M. Krieger, George Vachtsevanos, Brian Litt; (37):1025−1044, 2006.
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A Graphical Representation of Equivalence Classes of AMP Chain Graphs
Alberto Roverato, Milan Studený; (38):1045−1078, 2006.
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Action Elimination and Stopping Conditions for the Multi-Armed Bandit and Reinforcement Learning Problems
Eyal Even-Dar, Shie Mannor, Yishay Mansour; (39):1079−1105, 2006.
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Step Size Adaptation in Reproducing Kernel Hilbert Space
S. V. N. Vishwanathan, Nicol N. Schraudolph, Alex J. Smola; (40):1107−1133, 2006.
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New Algorithms for Efficient High-Dimensional Nonparametric Classification
Ting Liu, Andrew W. Moore, Alexander Gray; (41):1135−1158, 2006.
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A Very Fast Learning Method for Neural Networks Based on Sensitivity Analysis
Enrique Castillo, Bertha Guijarro-Berdiñas, Oscar Fontenla-Romero, Amparo Alonso-Betanzos; (42):1159−1182, 2006.
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Computational and Theoretical Analysis of Null Space and Orthogonal Linear Discriminant Analysis
Jieping Ye, Tao Xiong; (43):1183−1204, 2006.
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Worst-Case Analysis of Selective Sampling for Linear Classification
Nicoló Cesa-Bianchi, Claudio Gentile, Luca Zaniboni; (44):1205−1230, 2006.
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Nonparametric Quantile Estimation
Ichiro Takeuchi, Quoc V. Le, Timothy D. Sears, Alexander J. Smola; (45):1231−1264, 2006.
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The Interplay of Optimization and Machine Learning Research
Kristin P. Bennett, Emilio Parrado-Hernández; (46):1265−1281, 2006.
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Second Order Cone Programming Approaches for Handling Missing and Uncertain Data
Pannagadatta K. Shivaswamy, Chiranjib Bhattacharyya, Alexander J. Smola; (47):1283−1314, 2006.
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Ensemble Pruning Via Semi-definite Programming
Yi Zhang, Samuel Burer, W. Nick Street; (48):1315−1338, 2006.
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Linear Programs for Hypotheses Selection in Probabilistic Inference Models
Anders Bergkvist, Peter Damaschke, Marcel Lüthi; (49):1339−1355, 2006.
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Bayesian Network Learning with Parameter Constraints
Radu Stefan Niculescu, Tom M. Mitchell, R. Bharat Rao; (50):1357−1383, 2006.
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Learning Sparse Representations by Non-Negative Matrix Factorization and Sequential Cone Programming
Matthias Heiler, Christoph Schnörr; (51):1385−1407, 2006.
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Fast SDP Relaxations of Graph Cut Clustering, Transduction, and Other Combinatorial Problems
Tijl De Bie, Nello Cristianini; (52):1409−1436, 2006.
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Maximum-Gain Working Set Selection for SVMs
Tobias Glasmachers, Christian Igel; (53):1437−1466, 2006.
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Parallel Software for Training Large Scale Support Vector Machines on Multiprocessor Systems
Luca Zanni, Thomas Serafini, Gaetano Zanghirati; (54):1467−1492, 2006.
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Building Support Vector Machines with Reduced Classifier Complexity
S. Sathiya Keerthi, Olivier Chapelle, Dennis DeCoste; (55):1493−1515, 2006.
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Exact 1-Norm Support Vector Machines Via Unconstrained Convex Differentiable Minimization
Olvi L. Mangasarian; (56):1517−1530, 2006.
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Large Scale Multiple Kernel Learning
Sören Sonnenburg, Gunnar Rätsch, Christin Schäfer, Bernhard Schölkopf; (57):1531−1565, 2006.
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Efficient Learning of Label Ranking by Soft Projections onto Polyhedra
Shai Shalev-Shwartz, Yoram Singer; (58):1567−1599, 2006.
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Kernel-Based Learning of Hierarchical Multilabel Classification Models
Juho Rousu, Craig Saunders, Sandor Szedmak, John Shawe-Taylor; (59):1601−1626, 2006.
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Structured Prediction, Dual Extragradient and Bregman Projections
Ben Taskar, Simon Lacoste-Julien, Michael I. Jordan; (60):1627−1653, 2006.
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Active Learning with Feedback on Features and Instances
Hema Raghavan, Omid Madani, Rosie Jones; (61):1655−1686, 2006.
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Large Scale Transductive SVMs
Ronan Collobert, Fabian Sinz, Jason Weston, Léon Bottou; (62):1687−1712, 2006.
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Considering Cost Asymmetry in Learning Classifiers
Francis R. Bach, David Heckerman, Eric Horvitz; (63):1713−1741, 2006.
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Learning Factor Graphs in Polynomial Time and Sample Complexity
Pieter Abbeel, Daphne Koller, Andrew Y. Ng; (64):1743−1788, 2006.
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Collaborative Multiagent Reinforcement Learning by Payoff Propagation
Jelle R. Kok, Nikos Vlassis; (65):1789−1828, 2006.
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Estimating the “Wrong” Graphical Model: Benefits in the Computation-Limited Setting
Martin J. Wainwright; (66):1829−1859, 2006.
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Streamwise Feature Selection
Jing Zhou, Dean P. Foster, Robert A. Stine, Lyle H. Ungar; (67):1861−1885, 2006.
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Linear Programming Relaxations and Belief Propagation -- An Empirical Study
Chen Yanover, Talya Meltzer, Yair Weiss; (68):1887−1907, 2006.
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Incremental Support Vector Learning: Analysis, Implementation and Applications
Pavel Laskov, Christian Gehl, Stefan Krüger, Klaus-Robert Müller; (69):1909−1936, 2006.
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A Simulation-Based Algorithm for Ergodic Control of Markov Chains Conditioned on Rare Events
Shalabh Bhatnagar, Vivek S. Borkar, Madhukar Akarapu; (70):1937−1962, 2006.
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Learning Spectral Clustering, With Application To Speech Separation
Francis R. Bach, Michael I. Jordan; (71):1963−2001, 2006.
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A Linear Non-Gaussian Acyclic Model for Causal Discovery
Shohei Shimizu, Patrik O. Hoyer, Aapo Hyvärinen, Antti Kerminen; (72):2003−2030, 2006.
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Walk-Sums and Belief Propagation in Gaussian Graphical Models
Dmitry M. Malioutov, Jason K. Johnson, Alan S. Willsky; (73):2031−2064, 2006.
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Distance Patterns in Structural Similarity
Thomas Kämpke; (74):2065−2086, 2006.
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A Hierarchy of Support Vector Machines for Pattern Detection
Hichem Sahbi, Donald Geman; (75):2087−2123, 2006.
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Adaptive Prototype Learning Algorithms: Theoretical and Experimental Studies
Fu Chang, Chin-Chin Lin, Chi-Jen Lu; (76):2125−2148, 2006.
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A Scoring Function for Learning Bayesian Networks based on Mutual Information and Conditional Independence Tests
Luis M. de Campos; (77):2149−2187, 2006.
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Noisy-OR Component Analysis and its Application to Link Analysis
Tomáš Šingliar, Miloš Hauskrecht; (78):2189−2213, 2006.
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Learning a Hidden Hypergraph
Dana Angluin, Jiang Chen; (79):2215−2236, 2006.
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An Efficient Implementation of an Active Set Method for SVMs
Katya Scheinberg; (80):2237−2257, 2006.
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Causal Graph Based Decomposition of Factored MDPs
Anders Jonsson, Andrew Barto; (81):2259−2301, 2006.
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Accurate Error Bounds for the Eigenvalues of the Kernel Matrix
Mikio L. Braun; (82):2303−2328, 2006.
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Point-Based Value Iteration for Continuous POMDPs
Josep M. Porta, Nikos Vlassis, Matthijs T.J. Spaan, Pascal Poupart; (83):2329−2367, 2006.
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Learning Parts-Based Representations of Data
David A. Ross, Richard S. Zemel; (84):2369−2397, 2006.
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Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
Mikhail Belkin, Partha Niyogi, Vikas Sindhwani; (85):2399−2434, 2006.
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Consistency of Multiclass Empirical Risk Minimization Methods Based on Convex Loss
Di-Rong Chen, Tao Sun; (86):2435−2447, 2006.
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Bounds for the Loss in Probability of Correct Classification Under Model Based Approximation
Magnus Ekdahl, Timo Koski; (87):2449−2480, 2006.
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Estimation of Gradients and Coordinate Covariation in Classification
Sayan Mukherjee, Qiang Wu; (88):2481−2514, 2006.
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Expectation Correction for Smoothed Inference in Switching Linear Dynamical Systems
David Barber; (89):2515−2540, 2006.
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On Model Selection Consistency of Lasso
Peng Zhao, Bin Yu; (90):2541−2563, 2006.
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Stability Properties of Empirical Risk Minimization over Donsker Classes
Andrea Caponnetto, Alexander Rakhlin; (91):2565−2583, 2006.
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Linear State-Space Models for Blind Source Separation
Rasmus Kongsgaard Olsson, Lars Kai Hansen; (92):2585−2602, 2006.
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On Representing and Generating Kernels by Fuzzy Equivalence Relations
Bernhard Moser; (93):2603−2620, 2006.
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A Robust Procedure For Gaussian Graphical Model Search From Microarray Data With p Larger Than n
Robert Castelo, Alberto Roverato; (94):2621−2650, 2006.
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Universal Kernels
Charles A. Micchelli, Yuesheng Xu, Haizhang Zhang; (95):2651−2667, 2006.
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Machine Learning for Computer Security
Philip K. Chan, Richard P. Lippmann; (96):2669−2672, 2006.
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Spam Filtering Using Statistical Data Compression Models
Andrej Bratko, Gordon V. Cormack, Bogdan Filipič, Thomas R. Lynam, Blaž Zupan; (97):2673−2698, 2006.
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Spam Filtering Based On The Analysis Of Text Information Embedded Into Images
Giorgio Fumera, Ignazio Pillai, Fabio Roli; (98):2699−2720, 2006.
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Learning to Detect and Classify Malicious Executables in the Wild
J. Zico Kolter, Marcus A. Maloof; (99):2721−2744, 2006.
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On Inferring Application Protocol Behaviors in Encrypted Network Traffic
Charles V. Wright, Fabian Monrose, Gerald M. Masson; (100):2745−2769, 2006.
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© JMLR .