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ORCIDJournal 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 Bezerra de Andrade e Silva, Richard Scheines, Clark Glymour, Peter Spirtes:
Learning the Structure of Linear Latent Variable Models. 191-246 - Gilles Blanchard, Motoaki Kawanabe, Masashi Sugiyama, Vladimir G. 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 D. 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, Alexander 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 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 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 7, 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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