Volume 7,
January 2006
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
Volume 7,
April 2006
Volume 7,
May 2006
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
Machine Learning and Optimization
- 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
Volume 7,
September 2006
Machine Learning and Optimization
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
Machine Learning and Optimization
- 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
Machine Learning for Computer Security
- 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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