JMLR 2006

100 papers

On Inferring Application Protocol Behaviors in Encrypted Network Traffic

Charles V. Wright, Fabian Monrose, Gerald M. Masson

Learning to Detect and Classify Malicious Executables in the Wild

J. Zico Kolter, Marcus A. Maloof

Spam Filtering Based On The Analysis Of Text Information Embedded Into Images

Giorgio Fumera, Ignazio Pillai, Fabio Roli

Spam Filtering Using Statistical Data Compression Models

Andrej Bratko, Gordon V. Cormack, Bogdan Filipič, Thomas R. Lynam, Blaž Zupan

Machine Learning for Computer Security

Philip K. Chan, Richard P. Lippmann

Universal Kernels

Charles A. Micchelli, Yuesheng Xu, Haizhang Zhang

A Robust Procedure For Gaussian Graphical Model Search From Microarray Data With p Larger Than n

Robert Castelo, Alberto Roverato

On Representing and Generating Kernels by Fuzzy Equivalence Relations

Bernhard Moser

Linear State-Space Models for Blind Source Separation

Rasmus Kongsgaard Olsson, Lars Kai Hansen

Stability Properties of Empirical Risk Minimization over Donsker Classes

Andrea Caponnetto, Alexander Rakhlin

On Model Selection Consistency of Lasso

Peng Zhao, Bin Yu

Expectation Correction for Smoothed Inference in Switching Linear Dynamical Systems

David Barber

Estimation of Gradients and Coordinate Covariation in Classification

Sayan Mukherjee, Qiang Wu

Bounds for the Loss in Probability of Correct Classification Under Model Based Approximation

Magnus Ekdahl, Timo Koski

Consistency of Multiclass Empirical Risk Minimization Methods Based on Convex Loss

Di-Rong Chen, Tao Sun

Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples

Mikhail Belkin, Partha Niyogi, Vikas Sindhwani

Learning Parts-Based Representations of Data

David A. Ross, Richard S. Zemel

Point-Based Value Iteration for Continuous POMDPs

Josep M. Porta, Nikos Vlassis, Matthijs T.J. Spaan, Pascal Poupart

Accurate Error Bounds for the Eigenvalues of the Kernel Matrix

Mikio L. Braun

Causal Graph Based Decomposition of Factored MDPs

Anders Jonsson, Andrew Barto

An Efficient Implementation of an Active Set Method for SVMs

Katya Scheinberg

Learning a Hidden Hypergraph

Dana Angluin, Jiang Chen

Noisy-OR Component Analysis and its Application to Link Analysis

Tomáš &#352

A Scoring Function for Learning Bayesian Networks based on Mutual Information and Conditional Independence Tests

Luis M. de Campos

Adaptive Prototype Learning Algorithms: Theoretical and Experimental Studies

Fu Chang, Chin-Chin Lin, Chi-Jen Lu

A Hierarchy of Support Vector Machines for Pattern Detection

Hichem Sahbi, Donald Geman

Distance Patterns in Structural Similarity

Thomas K&#228

Walk-Sums and Belief Propagation in Gaussian Graphical Models

Dmitry M. Malioutov, Jason K. Johnson, Alan S. Willsky

A Linear Non-Gaussian Acyclic Model for Causal Discovery

Shohei Shimizu, Patrik O. Hoyer, Aapo Hyv&#228

Learning Spectral Clustering, With Application To Speech Separation

Francis R. Bach, Michael I. Jordan

A Simulation-Based Algorithm for Ergodic Control of Markov Chains Conditioned on Rare Events

Shalabh Bhatnagar, Vivek S. Borkar, Madhukar Akarapu

Incremental Support Vector Learning: Analysis, Implementation and Applications

Pavel Laskov, Christian Gehl, Stefan Krüger, Klaus-Robert Müller

Linear Programming Relaxations and Belief Propagation -- An Empirical Study

Chen Yanover, Talya Meltzer, Yair Weiss

Streamwise Feature Selection

Jing Zhou, Dean P. Foster, Robert A. Stine, Lyle H. Ungar

Estimating the “Wrong” Graphical Model: Benefits in the Computation-Limited Setting

Martin J. Wainwright

Collaborative Multiagent Reinforcement Learning by Payoff Propagation

Jelle R. Kok, Nikos Vlassis

Learning Factor Graphs in Polynomial Time and Sample Complexity

Pieter Abbeel, Daphne Koller, Andrew Y. Ng

Considering Cost Asymmetry in Learning Classifiers

Francis R. Bach, David Heckerman, Eric Horvitz

Large Scale Transductive SVMs

Ronan Collobert, Fabian Sinz, Jason Weston, Léon Bottou

Active Learning with Feedback on Features and Instances

Hema Raghavan, Omid Madani, Rosie Jones

Structured Prediction, Dual Extragradient and Bregman Projections

Ben Taskar, Simon Lacoste-Julien, Michael I. Jordan

Kernel-Based Learning of Hierarchical Multilabel Classification Models

Juho Rousu, Craig Saunders, Sandor Szedmak, John Shawe-Taylor

Efficient Learning of Label Ranking by Soft Projections onto Polyhedra

Shai Shalev-shwartz, Yoram Singer

Large Scale Multiple Kernel Learning

Sören Sonnenburg, Gunnar Rätsch, Christin Schäfer, Bernhard Schölkopf

Exact 1-Norm Support Vector Machines Via Unconstrained Convex Differentiable Minimization

Olvi L. Mangasarian

Building Support Vector Machines with Reduced Classifier Complexity

S. Sathiya Keerthi, Olivier Chapelle, Dennis DeCoste

Parallel Software for Training Large Scale Support Vector Machines on Multiprocessor Systems

Luca Zanni, Thomas Serafini, Gaetano Zanghirati

Maximum-Gain Working Set Selection for SVMs

Tobias Glasmachers, Christian Igel

Fast SDP Relaxations of Graph Cut Clustering, Transduction, and Other Combinatorial Problems

Tijl De Bie, Nello Cristianini

Learning Sparse Representations by Non-Negative Matrix Factorization and Sequential Cone Programming

Matthias Heiler, Christoph Schnörr

Bayesian Network Learning with Parameter Constraints

Radu Stefan Niculescu, Tom M. Mitchell, R. Bharat Rao

Linear Programs for Hypotheses Selection in Probabilistic Inference Models

Anders Bergkvist, Peter Damaschke, Marcel Lüthi

Ensemble Pruning Via Semi-definite Programming

Yi Zhang, Samuel Burer, W. Nick Street

Second Order Cone Programming Approaches for Handling Missing and Uncertain Data

Pannagadatta K. Shivaswamy, Chiranjib Bhattacharyya, Alexander J. Smola

The Interplay of Optimization and Machine Learning Research

Kristin P. Bennett, Emilio Parrado-hernández

Nonparametric Quantile Estimation

Ichiro Takeuchi, Quoc V. Le, Timothy D. Sears, Alexander J. Smola

Worst-Case Analysis of Selective Sampling for Linear Classification

Nicolò Cesa-bianchi, Claudio Gentile, Luca Zaniboni

Computational and Theoretical Analysis of Null Space and Orthogonal Linear Discriminant Analysis

Jieping Ye, Tao Xiong

A Very Fast Learning Method for Neural Networks Based on Sensitivity Analysis

Enrique Castillo, Bertha Guijarro-Berdiñas, Oscar Fontenla-Romero, Amparo Alonso-Betanzos

New Algorithms for Efficient High-Dimensional Nonparametric Classification

Ting Liu, Andrew W. Moore, Alexander Gray

Step Size Adaptation in Reproducing Kernel Hilbert Space

S. V. N. Vishwanathan, Nicol N. Schraudolph, Alex J. Smola

Action Elimination and Stopping Conditions for the Multi-Armed Bandit and Reinforcement Learning Problems

Eyal Even-dar, Shie Mannor, Yishay Mansour

A Graphical Representation of Equivalence Classes of AMP Chain Graphs

Alberto Roverato, Milan Studen&#253

One-Class Novelty Detection for Seizure Analysis from Intracranial EEG

Andrew B. Gardner, Abba M. Krieger, George Vachtsevanos, Brian Litt

Sparse Boosting

Peter Bühlmann, Bin Yu

Quantile Regression Forests

Nicolai Meinshausen

Lower Bounds and Aggregation in Density Estimation

Guillaume Lecue

Segmental Hidden Markov Models with Random Effects for Waveform Modeling

Seyoung Kim, Padhraic Smyth

Rearrangement Clustering: Pitfalls, Remedies, and Applications

Sharlee Climer, Weixiong Zhang

Evolutionary Function Approximation for Reinforcement Learning

Shimon Whiteson, Peter Stone

Infinite-Ï Limits For Tikhonov Regularization

Ross A. Lippert, Ryan M. Rifkin

Consistency and Convergence Rates of One-Class SVMs and Related Algorithms

Régis Vert, Jean-philippe Vert

Learning Image Components for Object Recognition

Michael W. Spratling

Policy Gradient in Continuous Time

Rémi Munos

QP Algorithms with Guaranteed Accuracy and Run Time for Support Vector Machines

Don Hush, Patrick Kelly, Clint Scovel, Ingo Steinwart

Some Theory for Generalized Boosting Algorithms

Peter J. Bickel, Ya'acov Ritov, Alon Zakai

Learning Minimum Volume Sets

Clayton D. Scott, Robert D. Nowak

Pattern Recognition for Conditionally Independent Data

Daniil Ryabko

Stochastic Complexities of Gaussian Mixtures in Variational Bayesian Approximation

Kazuho Watanabe, Sumio Watanabe

A Direct Method for Building Sparse Kernel Learning Algorithms

Mingrui Wu, Bernhard Schölkopf, Gökhan Bakir

Toward Attribute Efficient Learning of Decision Lists and Parities

Adam R. Klivans, Rocco A. Servedio

Online Passive-Aggressive Algorithms

Koby Crammer, Ofer Dekel, Joseph Keshet, Shai Shalev-shwartz, Yoram Singer

Learning Coordinate Covariances via Gradients

Sayan Mukherjee, Ding-Xuan Zhou

Learning Recursive Control Programs from Problem Solving

Pat Langley, Dongkyu Choi

Optimising Kernel Parameters and Regularisation Coefficients for Non-linear Discriminant Analysis

Tonatiuh Peña Centeno, Neil D. Lawrence

Inductive Synthesis of Functional Programs: An Explanation Based Generalization Approach

Emanuel Kitzelmann, Ute Schmid

Geometric Variance Reduction in Markov Chains: Application to Value Function and Gradient Estimation

Rémi Munos

Superior Guarantees for Sequential Prediction and Lossless Compression via Alphabet Decomposition

Ron Begleiter, Ran El-Yaniv

Using Machine Learning to Guide Architecture Simulation

Greg Hamerly, Erez Perelman, Jeremy Lau, Brad Calder, Timothy Sherwood

Kernels on Prolog Proof Trees: Statistical Learning in the ILP Setting

Andrea Passerini, Paolo Frasconi, Luc De Raedt

Some Discriminant-Based PAC Algorithms

Paul W. Goldberg

In Search of Non-Gaussian Components of a High-Dimensional Distribution

Gilles Blanchard, Motoaki Kawanabe, Masashi Sugiyama, Vladimir Spokoiny, Klaus-Robert Müller

Learning the Structure of Linear Latent Variable Models

Ricardo Silva, Richard Scheine, Clark Glymour, Peter Spirtes

MinReg: A Scalable Algorithm for Learning Parsimonious Regulatory Networks in Yeast and Mammals

Dana Pe'er, Amos Tanay, Aviv Regev

Active Learning in Approximately Linear Regression Based on Conditional Expectation of Generalization Error

Masashi Sugiyama

Bounds for Linear Multi-Task Learning

Andreas Maurer

Generalized Bradley-Terry Models and Multi-Class Probability Estimates

Tzu-kuo Huang, Ruby C. Weng, Chih-Jen Lin

On the Complexity of Learning Lexicographic Strategies

Michael Schmitt, Laura Martignon

Incremental Algorithms for Hierarchical Classification

Nicolò Cesa-bianchi, Claudio Gentile, Luca Zaniboni

Statistical Comparisons of Classifiers over Multiple Data Sets

Janez Demsar