JMLR 2005

73 papers

Expectation Consistent Approximate Inference

Manfred Opper, Ole Winther

On the Nystrom Method for Approximating a Gram Matrix for Improved Kernel-Based Learning

Petros Drineas, Michael W. Mahoney

Efficient Margin Maximizing with Boosting

Gunnar Rätsch, Manfred K. Warmuth

Kernel Methods for Measuring Independence

Arthur Gretton, Ralf Herbrich, Alexander Smola, Olivier Bousquet, Bernhard Sch&#246

Convergence Theorems for Generalized Alternating Minimization Procedures

Asela Gunawardana, William Byrne

Asymptotics in Empirical Risk Minimization

Leila Mohammadi, Sara van de Geer

Change Point Problems in Linear Dynamical Systems

Onno Zoeter, Tom Heskes

What's Strange About Recent Events (WSARE): An Algorithm for the Early Detection of Disease Outbreaks

Weng-Keen Wong, Andrew Moore, Gregory Cooper, Michael Wagner

A Unifying View of Sparse Approximate Gaussian Process Regression

Joaquin Quiñonero-Candela, Carl Edward Rasmussen

New Horn Revision Algorithms

Judy Goldsmith, Robert H. Sloan

Working Set Selection Using Second Order Information for Training Support Vector Machines

Rong-En Fan, Pai-Hsuen Chen, Chih-Jen Lin

Feature Selection for Unsupervised and Supervised Inference: The Emergence of Sparsity in a Weight-Based Approach

Lior Wolf, Amnon Shashua

A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data

Rie Kubota Ando, Tong Zhang

Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models

Neil Lawrence

Combining Information Extraction Systems Using Voting and Stacked Generalization

Georgios Sigletos, Georgios Paliouras, Constantine D. Spyropoulos, Michalis Hatzopoulos

Clustering with Bregman Divergences

Arindam Banerjee, Srujana Merugu, Inderjit S. Dhillon, Joydeep Ghosh

Assessing Approximate Inference for Binary Gaussian Process Classification

Malte Kuss, Carl Edward Rasmussen

Active Coevolutionary Learning of Deterministic Finite Automata

Josh Bongard, Hod Lipson

Managing Diversity in Regression Ensembles

Gavin Brown, Jeremy L. Wyatt, Peter Ti&#328

Fast Kernel Classifiers with Online and Active Learning

Antoine Bordes, Seyda Ertekin, Jason Weston, Léon Bottou

A Bayesian Model for Supervised Clustering with the Dirichlet Process Prior

Hal Daume III, Daniel Marcu

Local Propagation in Conditional Gaussian Bayesian Networks

Robert G. Cowell

Frames, Reproducing Kernels, Regularization and Learning

Alain Rakotomamonjy, Stéphane Canu

Large Margin Methods for Structured and Interdependent Output Variables

Ioannis Tsochantaridis, Thorsten Joachims, Thomas Hofmann, Yasemin Altun

A Bayes Optimal Approach for Partitioning the Values of Categorical Attributes

Marc Boullé

Maximum Margin Algorithms with Boolean Kernels

Roni Khardon, Rocco A. Servedio

Inner Product Spaces for Bayesian Networks

Atsuyoshi Nakamura, Michael Schmitt, Niels Schmitt, Hans Ulrich Simon

Clustering on the Unit Hypersphere using von Mises-Fisher Distributions

Arindam Banerjee, Inderjit S. Dhillon, Joydeep Ghosh, Suvrit Sra

Efficient Computation of Gapped Substring Kernels on Large Alphabets

Juho Rousu, John Shawe-Taylor

Universal Algorithms for Learning Theory Part I : Piecewise Constant Functions

Peter Binev, Albert Cohen, Wolfgang Dahmen, Ronald DeVore, Vladimir Temlyakov

An MDP-Based Recommender System

Guy Shani, David Heckerman, Ronen I. Brafman

Concentration Bounds for Unigram Language Models

Evgeny Drukh, Yishay Mansour

Separating a Real-Life Nonlinear Image Mixture

Luís B. Almeida

Semigroup Kernels on Measures

Marco Cuturi, Kenji Fukumizu, Jean-philippe Vert

Analysis of Variance of Cross-Validation Estimators of the Generalization Error

Marianthi Markatou, Hong Tian, Shameek Biswas, George Hripcsak

Learning the Kernel Function via Regularization

Charles A. Micchelli, Massimiliano Pontil

A Generalization Error for Q-Learning

Susan A. Murphy

Learning the Kernel with Hyperkernels

Cheng Soon Ong, Alexander J. Smola, Robert C. Williamson

Gaussian Processes for Ordinal Regression

Wei Chu, Zoubin Ghahramani

Matrix Exponentiated Gradient Updates for On-line Learning and Bregman Projection

Koji Tsuda, Gunnar Rätsch, Manfred K. Warmuth

Algorithmic Stability and Meta-Learning

Andreas Maurer

Learning a Mahalanobis Metric from Equivalence Constraints

Aharon Bar-Hillel, Tomer Hertz, Noam Shental, Daphna Weinshall

Loopy Belief Propagation: Convergence and Effects of Message Errors

Alexander T. Ihler, John W. Fisher III, Alan S. Willsky

Learning from Examples as an Inverse Problem

Ernesto De Vito, Lorenzo Rosasco, Andrea Caponnetto, Umberto De Giovannini, Francesca Odone

Prioritization Methods for Accelerating MDP Solvers

David Wingate, Kevin D. Seppi

Multiclass Classification with Multi-Prototype Support Vector Machines

Fabio Aiolli, Alessandro Sperduti

Machine Learning Methods for Predicting Failures in Hard Drives: A Multiple-Instance Application

Joseph F. Murray, Gordon F. Hughes, Kenneth Kreutz-Delgado

Quasi-Geodesic Neural Learning Algorithms Over the Orthogonal Group: A Tutorial

Simone Fiori

Smooth ε-Insensitive Regression by Loss Symmetrization

Ofer Dekel, Shai Shalev-shwartz, Yoram Singer

Estimation of Non-Normalized Statistical Models by Score Matching

Aapo Hyvärinen

Variational Message Passing

John Winn, Christopher M. Bishop

Adaptive Online Prediction by Following the Perturbed Leader

Marcus Hutter, Jan Poland

Learning Multiple Tasks with Kernel Methods

Theodoros Evgeniou, Charles A. Micchelli, Massimiliano Pontil

Active Learning to Recognize Multiple Types of Plankton

Tong Luo, Kurt Kramer, Dmitry B. Goldgof, Lawrence O. Hall, Scott Samson

Learning Module Networks

Eran Segal, Dana Pe'er, Aviv Regev, Daphne Koller, Nir Friedman

Tree-Based Batch Mode Reinforcement Learning

Damien Ernst, Pierre Geurts, Louis Wehenkel

Characterization of a Family of Algorithms for Generalized Discriminant Analysis on Undersampled Problems

Jieping Ye

Estimating Functions for Blind Separation When Sources Have Variance Dependencies

Motoaki Kawanabe, Klaus-Robert M&#252

Learning with Decision Lists of Data-Dependent Features

Mario Marchand, Marina Sokolova

Generalization Bounds for the Area Under the ROC Curve

Shivani Agarwal, Thore Graepel, Ralf Herbrich, Sariel Har-Peled, Dan Roth

Core Vector Machines: Fast SVM Training on Very Large Data Sets

Ivor W. Tsang, James T. Kwok, Pak-Ming Cheung

A Modified Finite Newton Method for Fast Solution of Large Scale Linear SVMs

S. Sathiya Keerthi, Dennis DeCoste

Generalization Bounds and Complexities Based on Sparsity and Clustering for Convex Combinations of Functions from Random Classes

Savina Andonova Jaeger

Tutorial on Practical Prediction Theory for Classification

John Langford

Denoising Source Separation

Jaakko Särelä, Harri Valpola

A Classification Framework for Anomaly Detection

Ingo Steinwart, Don Hush, Clint Scovel

Multiclass Boosting for Weak Classifiers

Günther Eibl, Karl-Peter Pfeiffer

Information Bottleneck for Gaussian Variables

Gal Chechik, Amir Globerson, Naftali Tishby, Yair Weiss

Diffusion Kernels on Statistical Manifolds

John Lafferty, Guy Lebanon

Learning Hidden Variable Networks: The Information Bottleneck Approach

Gal Elidan, Nir Friedman

Stability of Randomized Learning Algorithms

André Elisseeff, Theodoros Evgeniou, Massimiliano Pontil

Dimension Reduction in Text Classification with Support Vector Machines

Hyunsoo Kim, Peg Howland, Haesun Park

Asymptotic Model Selection for Naive Bayesian Networks

Dmitry Rusakov, Dan Geiger