JMLR 2007

91 papers

Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts

J. Zico Kolter, Marcus A. Maloof

A New Probabilistic Approach in Rank Regression with Optimal Bayesian Partitioning

Carine Hue, Marc Boullé

Stagewise Lasso

Peng Zhao, Bin Yu

Ranking the Best Instances

Stéphan Clémençon, Nicolas Vayatis

Hierarchical Average Reward Reinforcement Learning

Mohammad Ghavamzadeh, Sridhar Mahadevan

Learning in Environments with Unknown Dynamics: Towards more Robust Concept Learners

Marlon Núñez, Raúl Fidalgo, Rafael Morales

VC Theory of Large Margin Multi-Category Classifiers

Yann Guermeur

Revised Loss Bounds for the Set Covering Machine and Sample-Compression Loss Bounds for Imbalanced Data

Zakria Hussain, François Laviolette, Mario Marchand, John Shawe-Taylor, Spencer Charles Brubaker

Nonlinear Estimators and Tail Bounds for Dimension Reduction in l1 Using Cauchy Random Projections

Ping Li, Trevor J. Hastie, Kenneth W. Church

On the Representer Theorem and Equivalent Degrees of Freedom of SVR

Francesco Dinuzzo, Marta Neve, Giuseppe De Nicolao, Ugo Pietro Gianazza

The Need for Open Source Software in Machine Learning

Sören Sonnenburg, Mikio L. Braun, Cheng Soon Ong, Samy Bengio, Léon Bottou

The Locally Weighted Bag of Words Framework for Document Representation

Guy Lebanon, Yi Mao, Joshua Dillon

The On-Line Shortest Path Problem Under Partial Monitoring

András György, Tamás Linder, Gabor Lugosi, György Ottucsák

AdaBoost is Consistent

Peter L. Bartlett, Mikhail Traskin

Harnessing the Expertise of 70,000 Human Editors: Knowledge-Based Feature Generation for Text Categorization

Evgeniy Gabrilovich, Shaul Markovitch

Euclidean Embedding of Co-occurrence Data

Amir Globerson, Gal Chechik, Fernando Pereira, Naftali Tishby

Online Learning of Multiple Tasks with a Shared Loss

Ofer Dekel, Philip M. Long, Yoram Singer

Proto-value Functions: A Laplacian Framework for Learning Representation and Control in Markov Decision Processes

Sridhar Mahadevan, Mauro Maggioni

Transfer Learning via Inter-Task Mappings for Temporal Difference Learning

Matthew E. Taylor, Peter Stone, Yaxin Liu

A Complete Characterization of a Family of Solutions to a Generalized Fisher Criterion

Marco Loog

Refinable Kernels

Yuesheng Xu, Haizhang Zhang

Unlabeled Compression Schemes for Maximum Classes

Dima Kuzmin, Manfred K. Warmuth

Very Fast Online Learning of Highly Non Linear Problems

Aggelos Chariatis

Truncating the Loop Series Expansion for Belief Propagation

Vicenç Gómez, Joris M. Mooij, Hilbert J. Kappen

A Generalized Maximum Entropy Approach to Bregman Co-clustering and Matrix Approximation

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

Fast Iterative Kernel Principal Component Analysis

Simon Günter, Nicol N. Schraudolph, S. V. N. Vishwanathan

Large Margin Semi-supervised Learning

Junhui Wang, Xiaotong Shen

Behavioral Shaping for Geometric Concepts

Manu Chhabra, Robert A. Jacobs, Daniel &#352

"Ideal Parent” Structure Learning for Continuous Variable Bayesian Networks

Gal Elidan, Iftach Nachman, Nir Friedman

Characterizing the Function Space for Bayesian Kernel Models

Natesh S. Pillai, Qiang Wu, Feng Liang, Sayan Mukherjee, Robert L. Wolpert

Structure and Majority Classes in Decision Tree Learning

Ray J. Hickey

Polynomial Identification in the Limit of Substitutable Context-free Languages

Alexander Clark, Rémi Eyraud

A Nonparametric Statistical Approach to Clustering via Mode Identification

Jia Li, Surajit Ray, Bruce G. Lindsay

Compression-Based Averaging of Selective Naive Bayes Classifiers

Marc Boullé

Handling Missing Values when Applying Classification Models

Maytal Saar-Tsechansky, Foster Provost

Spherical-Homoscedastic Distributions: The Equivalency of Spherical and Normal Distributions in Classification

Onur C. Hamsici, Aleix M. Martinez

Multi-class Protein Classification Using Adaptive Codes

Iain Melvin, Eugene Ie, Jason Weston, William Stafford Noble, Christina Leslie

An Interior-Point Method for Large-Scale l1-Regularized Logistic Regression

Kwangmoo Koh, Seung-Jean Kim, Stephen Boyd

On the Effectiveness of Laplacian Normalization for Graph Semi-supervised Learning

Rie Johnson, Tong Zhang

PAC-Bayes Risk Bounds for Stochastic Averages and Majority Votes of Sample-Compressed Classifiers

François Laviolette, Mario Marchand

Attribute-Efficient and Non-adaptive Learning of Parities and DNF Expressions

Vitaly Feldman

Learning to Classify Ordinal Data: The Data Replication Method

Jaime S. Cardoso, Joaquim F. Pinto da Costa

Generalization Error Bounds in Semi-supervised Classification Under the Cluster Assumption

Philippe Rigollet

Graph Laplacians and their Convergence on Random Neighborhood Graphs

Matthias Hein, Jean-yves Audibert, Ulrike Von Luxburg

From External to Internal Regret

Avrim Blum, Yishay Mansour

Bayesian Quadratic Discriminant Analysis

Santosh Srivastava, Maya R. Gupta, Béla A. Frigyik

Measuring Differentiability: Unmasking Pseudonymous Authors

Moshe Koppel, Jonathan Schler, Elisheva Bonchek-Dokow

Maximum Entropy Density Estimation with Generalized Regularization and an Application to Species Distribution Modeling

Miroslav Dudík, Steven J. Phillips, Robert E. Schapire

Synergistic Face Detection and Pose Estimation with Energy-Based Models

Margarita Osadchy, Yann Le Cun, Matthew L. Miller

Local Discriminant Wavelet Packet Coordinates for Face Recognition

Chao-Chun Liu, Dao-Qing Dai, Hong Yan

Penalized Model-Based Clustering with Application to Variable Selection

Wei Pan, Xiaotong Shen

Loop Corrections for Approximate Inference on Factor Graphs

Joris M. Mooij, Hilbert J. Kappen

Bilinear Discriminant Component Analysis

Mads Dyrholm, Christoforos Christoforou, Lucas C. Parra

Undercomplete Blind Subspace Deconvolution

Zoltan Szabo, Barnabás Póczos, András L&#337

Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis

Masashi Sugiyama

On the Consistency of Multiclass Classification Methods

Ambuj Tewari, Peter L. Bartlett

Covariate Shift Adaptation by Importance Weighted Cross Validation

Masashi Sugiyama, Matthias Krauledat, Klaus-Robert Müller

Classification in Networked Data: A Toolkit and a Univariate Case Study

Sofus A. Macskassy, Foster Provost

Anytime Learning of Decision Trees

Saher Esmeir, Shaul Markovitch

Combining PAC-Bayesian and Generic Chaining Bounds

Jean-yves Audibert, Olivier Bousquet

Preventing Over-Fitting during Model Selection via Bayesian Regularisation of the Hyper-Parameters

Gavin C. Cawley, Nicola L. C. Talbot

Gini Support Vector Machine: Quadratic Entropy Based Robust Multi-Class Probability Regression

Shantanu Chakrabartty, Gert Cauwenberghs

Concave Learners for Rankboost

Ofer Melnik, Yehuda Vardi, Cun-hui Zhang

Sparseness vs Estimating Conditional Probabilities: Some Asymptotic Results

Peter L. Bartlett, Ambuj Tewari

Infinitely Imbalanced Logistic Regression

Art B. Owen

The Pyramid Match Kernel: Efficient Learning with Sets of Features

Kristen Grauman, Trevor Darrell

Dynamic Conditional Random Fields: Factorized Probabilistic Models for Labeling and Segmenting Sequence Data

Charles Sutton, Andrew McCallum, Khashayar Rohanimanesh

Relational Dependency Networks

Jennifer Neville, David Jensen

Margin Trees for High-dimensional Classification

Robert Tibshirani, Trevor Hastie

Estimating High-Dimensional Directed Acyclic Graphs with the PC-Algorithm

Markus Kalisch, Peter Bühlmann

Consistent Feature Selection for Pattern Recognition in Polynomial Time

Roland Nilsson, Jose M. Peña, Johan Björkegren, Jesper Tegnér

Learning Horn Expressions with LOGAN-H

Marta Arias, Roni Khardon, Jérôme Maloberti

A Stochastic Algorithm for Feature Selection in Pattern Recognition

Sébastien Gadat, Laurent Younes

Integrating NaÃ¯ve Bayes and FOIL

Niels Landwehr, Kristian Kersting, Luc De Raedt

Value Regularization and Fenchel Duality

Ryan M. Rifkin, Ross A. Lippert

Boosted Classification Trees and Class Probability/Quantile Estimation

David Mease, Abraham J. Wyner, Andreas Buja

Learning Equivariant Functions with Matrix Valued Kernels

Marco Reisert, Hans Burkhardt

Statistical Consistency of Kernel Canonical Correlation Analysis

Kenji Fukumizu, Francis R. Bach, Arthur Gretton

Dynamics and Generalization Ability of LVQ Algorithms

Michael Biehl, Anarta Ghosh, Barbara Hammer

General Polynomial Time Decomposition Algorithms

Nikolas List, Hans Ulrich Simon

Comments on the “Core Vector Machines: Fast SVM Training on Very Large Data Sets"

Gaëlle Loosli, Stéphane Canu

Separating Models of Learning from Correlated and Uncorrelated Data

Ariel Elbaz, Homin K. Lee, Rocco A. Servedio, Andrew Wan

Learnability of Gaussians with Flexible Variances

Yiming Ying, Ding-Xuan Zhou

Noise Tolerant Variants of the Perceptron Algorithm

Roni Khardon, Gabriel Wachman

A Probabilistic Analysis of EM for Mixtures of Separated, Spherical Gaussians

Sanjoy Dasgupta, Leonard Schulman

Building Blocks for Variational Bayesian Learning of Latent Variable Models

Tapani Raiko, Harri Valpola, Markus Harva, Juha Karhunen

Distances between Data Sets Based on Summary Statistics

Nikolaj Tatti

Minimax Regret Classifier for Imprecise Class Distributions

Rocío Alaiz-Rodríguez, Alicia Guerrero-Curieses, Jesús Cid-sueiro

A Unified Continuous Optimization Framework for Center-Based Clustering Methods

Marc Teboulle

Multi-Task Learning for Classification with Dirichlet Process Priors

Ya Xue, Xuejun Liao, Lawrence Carin, Balaji Krishnapuram

Nonlinear Boosting Projections for Ensemble Construction

Nicolás García-Pedrajas, César García-Osorio, Colin Fyfe