JMLR Volume 7

Statistical Comparisons of Classifiers over Multiple Data Sets
Janez Demšar; 7(Jan):1--30, 2006.
[abs][pdf]

Incremental Algorithms for Hierarchical Classification
Nicolò Cesa-Bianchi, Claudio Gentile, Luca Zaniboni; 7(Jan):31--54, 2006.
[abs][pdf]

On the Complexity of Learning Lexicographic Strategies
Michael Schmitt, Laura Martignon; 7(Jan):55--83, 2006.
[abs][pdf]

Generalized Bradley-Terry Models and Multi-Class Probability Estimates
Tzu-Kuo Huang, Ruby C. Weng, Chih-Jen Lin; 7(Jan):85--115, 2006.
[abs][pdf]

Bounds for Linear Multi-Task Learning
Andreas Maurer; 7(Jan):117--139, 2006.
[abs][pdf]

Active Learning in Approximately Linear Regression Based on Conditional Expectation of Generalization Error
Masashi Sugiyama; 7(Jan):141--166, 2006.
[abs][pdf]

MinReg: A Scalable Algorithm for Learning Parsimonious Regulatory Networks in Yeast and Mammals
Dana Pe'er, Amos Tanay, Aviv Regev; 7(Feb):167--189, 2006.
[abs][pdf]

Learning the Structure of Linear Latent Variable Models
Ricardo Silva, Richard Scheine, Clark Glymour, Peter Spirtes; 7(Feb):191--246, 2006.
[abs][pdf]

In Search of Non-Gaussian Components of a High-Dimensional Distribution
Gilles Blanchard, Motoaki Kawanabe, Masashi Sugiyama, Vladimir Spokoiny, Klaus-Robert Müller; 7(Feb):247--282, 2006.
[abs][pdf]

Some Discriminant-Based PAC Algorithms
Paul W. Goldberg; 7(Feb):283--306, 2006.
[abs][pdf]

Kernels on Prolog Proof Trees: Statistical Learning in the ILP Setting     (Special Topic on Inductive Programming)
Andrea Passerini, Paolo Frasconi, Luc De Raedt; 7(Feb):307--342, 2006.
[abs][pdf]

Using Machine Learning to Guide Architecture Simulation
Greg Hamerly, Erez Perelman, Jeremy Lau, Brad Calder, Timothy Sherwood; 7(Feb):343--378, 2006.
[abs][pdf]

Superior Guarantees for Sequential Prediction and Lossless Compression via Alphabet Decomposition
Ron Begleiter, Ran El-Yaniv; 7(Feb):379--411, 2006.
[abs][pdf]

Geometric Variance Reduction in Markov Chains: Application to Value Function and Gradient Estimation
Rémi Munos; 7(Feb):413--427, 2006.
[abs][pdf]

Inductive Synthesis of Functional Programs: An Explanation Based Generalization Approach     (Special Topic on Inductive Programming)
Emanuel Kitzelmann, Ute Schmid; 7(Feb):429--454, 2006.
[abs][pdf]

Optimising Kernel Parameters and Regularisation Coefficients for Non-linear Discriminant Analysis
Tonatiuh Peña Centeno, Neil D. Lawrence; 7(Feb):455--491, 2006.
[abs][pdf]

Learning Recursive Control Programs from Problem Solving     (Special Topic on Inductive Programming)
Pat Langley, Dongkyu Choi; 7(Mar):493--518, 2006.
[abs][pdf]

Learning Coordinate Covariances via Gradients
Sayan Mukherjee, Ding-Xuan Zhou; 7(Mar):519--549, 2006.
[abs][pdf]

Online Passive-Aggressive Algorithms
Koby Crammer, Ofer Dekel, Joseph Keshet, Shai Shalev-Shwartz, Yoram Singer; 7(Mar):551--585, 2006.
[abs][pdf]

Toward Attribute Efficient Learning of Decision Lists and Parities
Adam R. Klivans, Rocco A. Servedio; 7(Apr):587--602, 2006.
[abs][pdf]

A Direct Method for Building Sparse Kernel Learning Algorithms
Mingrui Wu, Bernhard Schölkopf, Gökhan Bakır; 7(Apr):603--624, 2006.
[abs][pdf]

Stochastic Complexities of Gaussian Mixtures in Variational Bayesian Approximation
Kazuho Watanabe, Sumio Watanabe; 7(Apr):625--644, 2006.
[abs][pdf]

Pattern Recognition for Conditionally Independent Data
Daniil Ryabko; 7(Apr):645--664, 2006.
[abs][pdf]

Learning Minimum Volume Sets
Clayton D. Scott, Robert D. Nowak; 7(Apr):665--704, 2006.
[abs][pdf]

Some Theory for Generalized Boosting Algorithms
Peter J. Bickel, Ya'acov Ritov, Alon Zakai; 7(May):705--732, 2006.
[abs][pdf]

QP Algorithms with Guaranteed Accuracy and Run Time for Support Vector Machines
Don Hush, Patrick Kelly, Clint Scovel, Ingo Steinwart; 7(May):733--769, 2006.
[abs][pdf]

Policy Gradient in Continuous Time
Rémi Munos; 7(May):771--791, 2006.
[abs][pdf]

Learning Image Components for Object Recognition
Michael W. Spratling; 7(May):793--815, 2006.
[abs][pdf]

Consistency and Convergence Rates of One-Class SVMs and Related Algorithms
Régis Vert, Jean-Philippe Vert; 7(May):817--854, 2006.
[abs][pdf]

Infinite-σ Limits For Tikhonov Regularization
Ross A. Lippert, Ryan M. Rifkin; 7(May):855--876, 2006.
[abs][pdf]

Evolutionary Function Approximation for Reinforcement Learning
Shimon Whiteson, Peter Stone; 7(May):877--917, 2006.
[abs][pdf]

Rearrangement Clustering: Pitfalls, Remedies, and Applications
Sharlee Climer, Weixiong Zhang; 7(Jun):919--943, 2006.
[abs][pdf]

Segmental Hidden Markov Models with Random Effects for Waveform Modeling
Seyoung Kim, Padhraic Smyth; 7(Jun):945--969, 2006.
[abs][pdf]

Lower Bounds and Aggregation in Density Estimation
Guillaume Lecué; 7(Jun):971--981, 2006.
[abs][pdf]

Quantile Regression Forests
Nicolai Meinshausen; 7(Jun):983--999, 2006.
[abs][pdf]

Sparse Boosting
Peter Bühlmann, Bin Yu; 7(Jun):1001--1024, 2006.
[abs][pdf]

One-Class Novelty Detection for Seizure Analysis from Intracranial EEG
Andrew B. Gardner, Abba M. Krieger, George Vachtsevanos, Brian Litt; 7(Jun):1025--1044, 2006.
[abs][pdf]

A Graphical Representation of Equivalence Classes of AMP Chain Graphs
Alberto Roverato, Milan Studený; 7(Jun):1045--1078, 2006.
[abs][pdf]

Action Elimination and Stopping Conditions for the Multi-Armed Bandit and Reinforcement Learning Problems
Eyal Even-Dar, Shie Mannor, Yishay Mansour; 7(Jun):1079--1105, 2006.
[abs][pdf]

Step Size Adaptation in Reproducing Kernel Hilbert Space
S. V. N. Vishwanathan, Nicol N. Schraudolph, Alex J. Smola; 7(Jun):1107--1133, 2006.
[abs][pdf]

New Algorithms for Efficient High-Dimensional Nonparametric Classification
Ting Liu, Andrew W. Moore, Alexander Gray; 7(Jun):1135--1158, 2006.
[abs][pdf]

A Very Fast Learning Method for Neural Networks Based on Sensitivity Analysis
Enrique Castillo, Bertha Guijarro-Berdiñas, Oscar Fontenla-Romero, Amparo Alonso-Betanzos; 7(Jul):1159--1182, 2006.
[abs][pdf]

Computational and Theoretical Analysis of Null Space and Orthogonal Linear Discriminant Analysis
Jieping Ye, Tao Xiong; 7(Jul):1183--1204, 2006.
[abs][pdf]

Worst-Case Analysis of Selective Sampling for Linear Classification
Nicolò Cesa-Bianchi, Claudio Gentile, Luca Zaniboni; 7(Jul):1205--1230, 2006.
[abs][pdf]

Nonparametric Quantile Estimation
Ichiro Takeuchi, Quoc V. Le, Timothy D. Sears, Alexander J. Smola; 7(Jul):1231--1264, 2006.
[abs][pdf]

The Interplay of Optimization and Machine Learning Research     (Special Topic on Machine Learning and Optimization)
Kristin P. Bennett, Emilio Parrado-Hernández; 7(Jul):1265--1281, 2006.
[abs][pdf]

Second Order Cone Programming Approaches for Handling Missing and Uncertain Data     (Special Topic on Machine Learning and Optimization)
Pannagadatta K. Shivaswamy, Chiranjib Bhattacharyya, Alexander J. Smola; 7(Jul):1283--1314, 2006.
[abs][pdf]

Ensemble Pruning Via Semi-definite Programming     (Special Topic on Machine Learning and Optimization)
Yi Zhang, Samuel Burer, W. Nick Street; 7(Jul):1315--1338, 2006.
[abs][pdf]

Linear Programs for Hypotheses Selection in Probabilistic Inference Models     (Special Topic on Machine Learning and Optimization)
Anders Bergkvist, Peter Damaschke, Marcel Lüthi; 7(Jul):1339--1355, 2006.
[abs][pdf]

Bayesian Network Learning with Parameter Constraints     (Special Topic on Machine Learning and Optimization)
Radu Stefan Niculescu, Tom M. Mitchell, R. Bharat Rao; 7(Jul):1357--1383, 2006.
[abs][pdf]

Learning Sparse Representations by Non-Negative Matrix Factorization and Sequential Cone Programming     (Special Topic on Machine Learning and Optimization)
Matthias Heiler, Christoph Schnörr; 7(Jul):1385--1407, 2006.
[abs][pdf]

Fast SDP Relaxations of Graph Cut Clustering, Transduction, and Other Combinatorial Problems     (Special Topic on Machine Learning and Optimization)
Tijl De Bie, Nello Cristianini; 7(Jul):1409--1436, 2006.
[abs][pdf]

Maximum-Gain Working Set Selection for SVMs     (Special Topic on Machine Learning and Optimization)
Tobias Glasmachers, Christian Igel; 7(Jul):1437--1466, 2006.
[abs][pdf]

Parallel Software for Training Large Scale Support Vector Machines on Multiprocessor Systems     (Special Topic on Machine Learning and Optimization)
Luca Zanni, Thomas Serafini, Gaetano Zanghirati; 7(Jul):1467--1492, 2006.
[abs][pdf]

Building Support Vector Machines with Reduced Classifier Complexity     (Special Topic on Machine Learning and Optimization)
S. Sathiya Keerthi, Olivier Chapelle, Dennis DeCoste; 7(Jul):1493--1515, 2006.
[abs][pdf]

Exact 1-Norm Support Vector Machines Via Unconstrained Convex Differentiable Minimization     (Special Topic on Machine Learning and Optimization)
Olvi L. Mangasarian; 7(Jul):1517--1530, 2006.
[abs][pdf]

Large Scale Multiple Kernel Learning     (Special Topic on Machine Learning and Optimization)
Sören Sonnenburg, Gunnar Rätsch, Christin Schäfer, Bernhard Schölkopf; 7(Jul):1531--1565, 2006.
[abs][pdf]

Efficient Learning of Label Ranking by Soft Projections onto Polyhedra     (Special Topic on Machine Learning and Optimization)
Shai Shalev-Shwartz, Yoram Singer; 7(Jul):1567--1599, 2006.
[abs][pdf]    [code]

Kernel-Based Learning of Hierarchical Multilabel Classification Models     (Special Topic on Machine Learning and Optimization)
Juho Rousu, Craig Saunders, Sandor Szedmak, John Shawe-Taylor; 7(Jul):1601--1626, 2006.
[abs][pdf]

Structured Prediction, Dual Extragradient and Bregman Projections     (Special Topic on Machine Learning and Optimization)
Ben Taskar, Simon Lacoste-Julien, Michael I. Jordan; 7(Jul):1627--1653, 2006.
[abs][pdf]

Active Learning with Feedback on Features and Instances
Hema Raghavan, Omid Madani, Rosie Jones; 7(Aug):1655--1686, 2006.
[abs][pdf]

Large Scale Transductive SVMs
Ronan Collobert, Fabian Sinz, Jason Weston, Léon Bottou; 7(Aug):1687--1712, 2006.
[abs][pdf]

Considering Cost Asymmetry in Learning Classifiers
Francis R. Bach, David Heckerman, Eric Horvitz; 7(Aug):1713--1741, 2006.
[abs][pdf]

Learning Factor Graphs in Polynomial Time and Sample Complexity
Pieter Abbeel, Daphne Koller, Andrew Y. Ng; 7(Aug):1743--1788, 2006.
[abs][pdf]

Collaborative Multiagent Reinforcement Learning by Payoff Propagation
Jelle R. Kok, Nikos Vlassis; 7(Sep):1789--1828, 2006.
[abs][pdf]

Estimating the "Wrong" Graphical Model: Benefits in the Computation-Limited Setting
Martin J. Wainwright; 7(Sep):1829--1859, 2006.
[abs][pdf]

Streamwise Feature Selection
Jing Zhou, Dean P. Foster, Robert A. Stine, Lyle H. Ungar; 7(Sep):1861--1885, 2006.
[abs][pdf]

Linear Programming Relaxations and Belief Propagation -- An Empirical Study     (Special Topic on Machine Learning and Optimization)
Chen Yanover, Talya Meltzer, Yair Weiss; 7(Sep):1887--1907, 2006.
[abs][pdf]    [data]

Incremental Support Vector Learning: Analysis, Implementation and Applications     (Special Topic on Machine Learning and Optimization)
Pavel Laskov, Christian Gehl, Stefan Krüger, Klaus-Robert Müller; 7(Sep):1909--1936, 2006.
[abs][pdf]

A Simulation-Based Algorithm for Ergodic Control of Markov Chains Conditioned on Rare Events
Shalabh Bhatnagar, Vivek S. Borkar, Madhukar Akarapu; 7(Oct):1937--1962, 2006.
[abs][pdf]

Learning Spectral Clustering, With Application To Speech Separation
Francis R. Bach, Michael I. Jordan; 7(Oct):1963--2001, 2006.
[abs][pdf]

A Linear Non-Gaussian Acyclic Model for Causal Discovery
Shohei Shimizu, Patrik O. Hoyer, Aapo Hyvärinen, Antti Kerminen; 7(Oct):2003--2030, 2006.
[abs][pdf]

Walk-Sums and Belief Propagation in Gaussian Graphical Models
Dmitry M. Malioutov, Jason K. Johnson, Alan S. Willsky; 7(Oct):2031--2064, 2006.
[abs][pdf]

Distance Patterns in Structural Similarity
Thomas Kämpke; 7(Oct):2065--2086, 2006.
[abs][pdf]

A Hierarchy of Support Vector Machines for Pattern Detection
Hichem Sahbi, Donald Geman; 7(Oct):2087--2123, 2006.
[abs][pdf]

Adaptive Prototype Learning Algorithms: Theoretical and Experimental Studies
Fu Chang, Chin-Chin Lin, Chi-Jen Lu; 7(Oct):2125--2148, 2006.
[abs][pdf]

A Scoring Function for Learning Bayesian Networks based on Mutual Information and Conditional Independence Tests
Luis M. de Campos; 7(Oct):2149--2187, 2006.
[abs][pdf]

Noisy-OR Component Analysis and its Application to Link Analysis
Tomáš Šingliar, Miloš Hauskrecht; 7(Oct):2189--2213, 2006.
[abs][pdf]

Learning a Hidden Hypergraph
Dana Angluin, Jiang Chen; 7(Oct):2215--2236, 2006.
[abs][pdf]

An Efficient Implementation of an Active Set Method for SVMs    (Special Topic on Machine Learning and Optimization)
Katya Scheinberg; 7(Oct):2237--2257, 2006.
[abs][pdf]

Causal Graph Based Decomposition of Factored MDPs
Anders Jonsson, Andrew Barto; 7(Nov):2259--2301, 2006.
[abs][pdf]

Accurate Error Bounds for the Eigenvalues of the Kernel Matrix
Mikio L. Braun; 7(Nov):2303--2328, 2006.
[abs][pdf]

Point-Based Value Iteration for Continuous POMDPs
Josep M. Porta, Nikos Vlassis, Matthijs T.J. Spaan, Pascal Poupart; 7(Nov):2329--2367, 2006.
[abs][pdf]

Learning Parts-Based Representations of Data
David A. Ross, Richard S. Zemel; 7(Nov):2369--2397, 2006.
[abs][pdf]

Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
Mikhail Belkin, Partha Niyogi, Vikas Sindhwani; 7(Nov):2399--2434, 2006.
[abs][pdf]

Consistency of Multiclass Empirical Risk Minimization Methods Based on Convex Loss
Di-Rong Chen, Tao Sun; 7(Nov):2435--2447, 2006.
[abs][pdf]

Bounds for the Loss in Probability of Correct Classification Under Model Based Approximation
Magnus Ekdahl, Timo Koski; 7(Nov):2449--2480, 2006.
[abs][pdf]

Estimation of Gradients and Coordinate Covariation in Classification
Sayan Mukherjee, Qiang Wu; 7(Nov):2481--2514, 2006.
[abs][pdf]

Expectation Correction for Smoothed Inference in Switching Linear Dynamical Systems
David Barber; 7(Nov):2515--2540, 2006.
[abs][pdf]

On Model Selection Consistency of Lasso
Peng Zhao, Bin Yu; 7(Nov):2541--2563, 2006.
[abs][pdf]

Stability Properties of Empirical Risk Minimization over Donsker Classes
Andrea Caponnetto, Alexander Rakhlin; 7(Dec):2565--2583, 2006.
[abs][pdf]

Linear State-Space Models for Blind Source Separation
Rasmus Kongsgaard Olsson, Lars Kai Hansen; 7(Dec):2585--2602, 2006.
[abs][pdf]

On Representing and Generating Kernels by Fuzzy Equivalence Relations
Bernhard Moser; 7(Dec):2603--2620, 2006.
[abs][pdf]

A Robust Procedure For Gaussian Graphical Model Search From Microarray Data With p Larger Than n
Robert Castelo, Alberto Roverato; 7(Dec):2621--2650, 2006.
[abs][pdf]

Universal Kernels
Charles A. Micchelli, Yuesheng Xu, Haizhang Zhang; 7(Dec):2651--2667, 2006.
[abs][pdf]

Machine Learning for Computer Security    (Special Topic on Machine Learning for Computer Security)
Philip K. Chan, Richard P. Lippmann; 7(Dec):2669--2672, 2006.
[abs][pdf]

Spam Filtering Using Statistical Data Compression Models     (Special Topic on Machine Learning for Computer Security)
Andrej Bratko, Gordon V. Cormack, Bogdan Filipič, Thomas R. Lynam, Blaž Zupan; 7(Dec):2673--2698, 2006.
[abs][pdf]

Spam Filtering Based On The Analysis Of Text Information Embedded Into Images     (Special Topic on Machine Learning for Computer Security)
Giorgio Fumera, Ignazio Pillai, Fabio Roli; 7(Dec):2699--2720, 2006.
[abs][pdf]

Learning to Detect and Classify Malicious Executables in the Wild     (Special Topic on Machine Learning for Computer Security)
J. Zico Kolter, Marcus A. Maloof; 7(Dec):2721--2744, 2006.
[abs][pdf]

On Inferring Application Protocol Behaviors in Encrypted Network Traffic     (Special Topic on Machine Learning for Computer Security)
Charles V. Wright, Fabian Monrose, Gerald M. Masson; 7(Dec):2745--2769, 2006.
[abs][pdf]




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