Accepted Papers of ICML 2004

Please refer to papers in this volume as:
Russ Greiner, Dale Schuurmans (Eds.) Proceedings of the 21st International Machine Learning Conference, ACM Press, 2004

A Comparative Study on Methods for Reducing Myopia of Hill-Climbing Search in Multirelational Learning
[Abstract][Paper]
	Lourdes Pena Castillo - Otto-von-Guericke-University Magdeburg Stefan Wrobel - Fraunhofer AIS and University Bonn
Active Learning of Label Ranking Functions
[Abstract][Paper]
	Klaus Brinker - University of Paderborn
Active Learning Using Pre-clustering
[Abstract][Paper]
	Hieu Nguyen - University of Amsterdam Arnold Smeulders - University of Amsterdam
Adaptive Cognitive Orthotics: Combining Reinforcement Learning and Constraint-Based Temporal Reasoning
[Abstract][Paper]
	Matthew Rudary - University of Michigan Satinder Singh - University of Michigan Martha Pollack - University of Michigan
A Fast Iterative Algorithm for Fisher Discriminant using Heterogeneous Kernels
[Abstract][Paper]
	Glenn Fung - CAD, Siemens Medical Solutions USA Murat Dundar - CAD, Siemens Medical Solutions USA Jinbo Bi - CAD, Siemens Medical Solutions USA Bharat Rao - CAD, Siemens Medical Solutions USA
A Graphical Model for Protein Secondary Structure Prediction
[Abstract][Paper]
	Wei Chu - Gatsby Computational Neuroscience Unit, University College London Zoubin Ghahramani - Gatsby Computational Neuroscience Unit, University College London David Wild - Keck Graduate Institute of Applied Life Sciences
A Hierarchical Method for Multi-Class Support Vector Machines
[Abstract][Paper]
	Volkan Vural - Northeastern University Jennifer Dy - Northeastern University
A Kernel View of the Dimensionality Reduction of Manifolds
[Abstract][Paper]
	Jihun Ham - University of Pennsylvania Daniel Lee - University of Pennsylvania Sebastian Mika - Fraunhofer FIRST.IDA Bernhard Schoelkopf - Max-Planck-Institut fuer biologische Kybernetik
A Maximum Entropy Approach to Species Distribution Modeling
[Abstract][Paper]
	Steven Phillips - AT&T Labs - Research Miroslav Dudik - Princeton University Robert Schapire - Princeton University
A MFoM Learning Approach to Robust Multiclass Multi-Label Text Categorization
[Abstract][Paper]
	sheng Gao - Institute for Infocomm Research, Singapore Wen Wu - Language Technologies Institute, School of Computer Science, Carnegie Mellon University, USA Chin - School of Electrical and Computer Engineering, Georgia Institute of Technology, USA Tat - School of Computing, National University of Singapore
A Monte Carlo Analysis of Ensemble Classification
[Abstract][Paper]
	Roberto Esposito - Università di Torino Lorenza Saitta - Università del Piemonte Orientale
A Multiplicative Up-Propagation Algorithm
[Abstract][Paper]
	Jong - POSTECH Seungjin Choi - POSTECH Jong - POSTECH
A Needle in a Haystack: Local One-Class Optimization
[Abstract][Paper]
	Koby Crammer - Hebrew University Gal Chechik - Stanford University
An Information Theoretic Analysis of Maximum Likelihood Mixture Estimation for Exponential Families
[Abstract][Paper]
	Arindam Banerjee - University of Texas at Austin Inderjit Dhillon - University of Texas at Austin Joydeep Ghosh - University of Texas at Austin Srujana Merugu - University of Texas at Austin
A Pitfall and Solution in Multi-Class Feature Selection for Text Classification
[Abstract][Paper]
	George Forman - Hewlett-Packard Labs
Apprenticeship Learning by Inverse Reinforcement Learning
[Abstract][Paper]
	Pieter Abbeel - Stanford University Andrew Ng - Stanford University
Approximate Inference by Markov Chains on Union Spaces
[Abstract][Paper]
	Max Welling - University of California Irvine Michal Rosen - University of California Irvine Yee Whye Teh - University of California in Berkeley
A Spatio-temporal Extension to Isomap Nonlinear Dimension Reduction
[Abstract][Paper]
	Odest Jenkins - University of Southern California Maja Mataric - University of Southern California
A Theoretical Characterization of Linear SVM-Based Feature Selection
[Abstract][Paper]
	Douglas Hardin - Vanderbilt University Ioannis Tsamardinos - Vanderbilt University Constantin Aliferis - Vanderbilt University
Authorship Verification as a One-Class Classification Problem
[Abstract][Paper]
	Moshe Koppel - Bar-Ilan University Jonathan Schler - Bar-Ilan University
Automated Hierarchical Mixtures of Probabilistic Principal Component Analyzers
[Abstract][Paper]
	Ting Su - Northeastern University Jennifer Dy - Northeastern University
Bayesian Haplotype Inference via the Dirichlet Process
[Abstract][Paper]
	Eric Xing - University of California, Berkeley Roded Sharan - International Computer Science Institute Michael Jordan - University of California, Berkeley
Bayesian Inference for Transductive Learning of Kernel Matrix Using the Tanner-Wong Data Augmentation Algorithm
[Abstract][Paper]
	Zhihua Zhang - Hong Kong University of Science and Technology Dit - Hong Kong University of Science and Technology James T. Kwok - Hong Kong University of Science and Technology
Bellman goes Relational
[Abstract][Paper]
	Kristian Kersting - University of Freiburg Martijn van Otterlo - University of Twente Luc De Raedt - University of Freiburg
Bias and Variance in Value Function Estimation
[Abstract][Paper]
	Shie Mannor - MIT Duncan Simester - MIT Peng Sun - Duke John Tsitsiklis - MIT
Boosting Grammatical Inference with Confidence Oracles
[Abstract][Paper]
	Jean - EURISE-Faculty of Sciences-University of Saint-Etienne Richard Nock - GRIMAAG-French West Indies and Guyana University Marc Sebban - EURISE-Faculty of Sciences-University of Saint-Etienne Henri - EURISE-Faculty of Sciences-University of Saint-Etienne
Boosting Margin Based Distance Functions for Clustering
[Abstract][Paper]
	Tomer Hertz - Hebrew University, Jerusalem Israel Aharon Bar - Hebrew University, Jerusalem Israel Daphna Weinshall - Hebrew University, Jerusalem Israel
C4.5 Competence Map: A Phase Transition-inspired Approach
[Abstract][Paper]
	Nicolas Baskiotis - LRI, Universite Paris-Sud Michele Sebag - LRI, Universite Paris-Sud
Coalition Calculation in a Dynamic Agent Environment
[Abstract][Paper]
	Ted Scully - National University of Ireland, Galway Michael G. Madden - National University of Ireland, Galway Gerard Lyons - National University of Ireland, Galway
Co-EM Support Vector Learning
[Abstract][Paper]
	Ulf Brefeld - Humboldt-Universitaet zu Berlin Tobias Scheffer - Humboldt-Universitaet zu Berlin
Communication Complexity as a Lower Bound for Learning in Games
[Abstract][Paper]
	Vincent Conitzer - Carnegie Mellon University Tuomas Sandholm - Carnegie Mellon University
Convergence of Synchronous Reinforcement Learning with Linear Function Approximation
[Abstract][Paper]
	Artur Merke - University of Dortmund Ralf Schoknecht - University of Karlsruhe
Decentralized Detection and Classification using Kernel Method
[Abstract][Paper]
	XuanLong Nguyen - U.C. Berkeley Martin Wainwright - U.C. Berkeley Michael Jordan - U.C. Berkeley
Decision Trees with Minimal Costs
[Abstract][Paper]
	Charles X. Ling - The University of Western Ontario Qiang Yang - The Hong Kong University of Science & Technology Jianning Wang - The University of Western Ontario Shichao Zhang - Guangxi Normal University
Delegating Classifiers
[Abstract][Paper]
	Cesar Ferri - Universitat Politecnica de Valencia Peter Flach - University of Bristol Jose Hernandez - Universitat Politecnica de Valencia
Distribution Kernels Based on Moments of Counts
[Abstract][Paper]
	Corinna Cortes - Google Labs Mehryar Mohri - AT&T Labs - Research
Diverse Ensembles for Active Learning
[Abstract][Paper]
	Prem Melville - University of Texas at Austin Raymond Mooney - University of Texas at Austin
Dynamic Abstraction in Reinforcement Learning via Clustering
[Abstract][Paper]
	Shie Mannor - MIT Ishai Menache - Technion Amit Hoze - Technion Uri Klein - Technion
Dynamic Conditional Random Fields: Factorized Probabilistic Models for Labeling and Segmenting Sequence Data
[Abstract][Paper]
	Charles Sutton - University of Massachusetts Khasayar Rohanimanesh - University of Massachusetts Andrew McCallum - University of Massachusetts
Efficient hierarchical MCMC for policy search
[Abstract][Paper]
	Malcolm Strens - QinetiQ Ltd.
Ensemble Selection from Libraries of Models
[Abstract][Paper]
	Rich Caruana - Cornell University Alexandru Niculescu - Cornell University Geoff Crew - Cornell University Alex Ksikes - Cornell University
Ensembles of Nested Dichotomies for Multi-class Problems
[Abstract][Paper]
	Eibe Frank - University of Waikato Stefan Kramer - Technical University of Munich
Entropy-Based Criterion in Categorical Clustering
[Abstract][Paper]
	Tao Li - University of Rochester Sheng Ma - IBM Research Mitsunori Ogihara - University of Rochester
Estimating Replicability of Classifier Learning Experiments
[Abstract][Paper]
	Remco Bouckaert - University of Waikato
Extensions of Marginalized Graph Kernels
[Abstract][Paper]
	Pierre Mahé - Ecole des Mines de Paris Nobuhisa Ueda - Kyoto University Tatsuya Akutsu - Kyoto University Jean - Kyoto University Jean - Ecole des Mines de Paris
Feature Extraction via Generalized Uncorrelated Linear Discriminant Analysis
[Abstract][Paper]
	Jieping Ye - University of Minnesota Ravi Janardan - University of Minnesota Qi Li - University of Delaware Haesun Park - University of Minnesota
Feature selection, L1 vs. L2 regularization, and rotational invariance
[Abstract][Paper]
	Andrew Ng - Stanford University
Feature Subset Selection for Learning Preferences: A Case Study
[Abstract][Paper]
	Antonio Bahamonde - Centro de Inteligencia Artificial. Universidad de Oviedo at Gijón, Spain Gustavo Bayón - Centro de Inteligencia Artificial. Universidad de Oviedo at Gijón, Spain Jorge Díez - Centro de Inteligencia Artificial. Universidad de Oviedo at Gijón, Spain José R. Quevedo - Centro de Inteligencia Artificial. Universidad de Oviedo at Gijón, Spain Oscar Luaces - Centro de Inteligencia Artificial. Universidad de Oviedo at Gijón, Spain Juan José del Coz - Centro de Inteligencia Artificial. Universidad de Oviedo at Gijón, Spain Jaime Alonso - Centro de Inteligencia Artificial. Universidad de Oviedo at Gijón, Spain Félix Goyache - SERIDA-CENSYRA-Somió, Spain
Gaussian Process Classification for Segmenting and Annotating Sequences
[Abstract][Paper]
	Yasemin Altun - Brown University Thomas Hofmann - Brown University Alexander Smola - Australian National University
Generalized Low Rank Approximations of Matrices
[Abstract][Paper]
	Jieping Ye - University of Minnesota
Generative Modeling for Continuous Non-Linearly Embedded Visual Inference
[Abstract][Paper]
	Cristian Sminchisescu - University of Toronto Allan Jepson - University of Toronto
Gradient LASSO for feature selection
[Abstract][Paper]
	Yongdai Kim - Seoul National University, Korea Jinseog Kim - Seoul National University, Korea
Hyperplane Margin Classifiers on the Multinomial Manifold
[Abstract][Paper]
	Guy Lebanon - Carnegie Mellon University John Lafferty - Carnegie Mellon University
Improving SVM Accuracy by Training on Auxiliary Data Sources
[Abstract][Paper]
	Pengcheng Wu - Oregon State University Thomas Dietterich - Oregon State University
Incremental Learning of Linear Model Trees
[Abstract][Paper]
	Duncan Potts - University of New South Wales
Integrating Constraints and Metric Learning in Semi-Supervised Clustering
[Abstract][Paper]
	Mikhail Bilenko - Department of Computer Sciences, University of Texas at Austin Sugato Basu - Department of Computer Sciences, University of Texas at Austin Raymond J. Mooney - Department of Computer Sciences, University of Texas at Austin
Interpolation-based Q-learning
[Abstract][Paper]
	Csaba Szepesvari - Computer and Automation Research Institute of the Hungarian Academy of Sciences William D. Smart - Department of Computer Science and Engineering, Washington University in St. Louis
Kernel-Based Discriminative Learning Algorithms for Labeling Sequences, Trees, and Graphs
[Abstract][Paper]
	Hisashi Kashima - IBM Tokyo Research Laboratory Yuta Tsuboi - IBM Tokyo Research Laboratory
Kernel Conditional Random Fields: Representation and Clique Selection
[Abstract][Paper]
	John Lafferty - School of Computer Science, Carnegie Mellon University Xiaojin Zhu - School of Computer Science, Carnegie Mellon University Yan Liu - School of Computer Science, Carnegie Mellon University
K-means Clustering via Principal Component Analysis
[Abstract][Paper]
	Chris Ding - Lawrence Berkeley National Laboratory Xiaofeng He - Lawrence Berkeley National Laboratory
Large Margin Hierarchical Classification
[Abstract][Paper]
	Ofer Dekel - The Hebrew University Joseph Keshet - The Hebrew University Yoram Singer - The Hebrew University
Learning a Kernel Matrix for Nonlinear Dimensionality Reduction
[Abstract][Paper]
	Kilian Weinberger - University of Pennsylvania Fei Sha - University of Pennsylvania Lawrence Saul - University of Pennsylvania
Learning and Discovery of Predictive State Representations in Dynamical Systems with Reset
[Abstract][Paper]
	Michael James - University of Michigan Satinder Singh - University of Michigan
Learning and Evaluating Classifiers under Sample Selection Bias
[Abstract][Paper]
	Bianca Zadrozny - IBM T.J. Watson Research Center
Learning Associative Markov Networks
[Abstract][Paper]
	Ben Taskar - Stanford University Vassil Chatalbashev - Stanford University Daphne Koller - Stanford University
Learning Bayesian Network Classifiers by Maximizing Conditional Likelihood
[Abstract][Paper]
	Daniel Grossman - University of Washington Pedro Domingos - University of Washington
Learning First-order Rules from Data with Multiple Parts: Applications on Mining Chemical Compound Data
[Abstract][Paper]
	Cholwich Nattee - Osaka University Sukree Sinthupinyo - Osaka University Masayuki Numao - Osaka University Takashi Okada - Kwansei Gakuin University
Learning Large Margin Classifiers Locally and Globally
[Abstract][Paper]
	Kaizhu Huang - The Chinese University of Hong Kong Haiqin Yang - The Chinese University of Hong Kong Irwin King - The Chinese University of Hong Kong Michael R. Lyu - The Chinese University of Hong Kong
Learning Low Dimensional Predictive Representations
[Abstract][Paper]
	Matthew Rosencrantz - Carnegie Mellon University Geoffrey Gordon - Carnegie Mellon University Sebastian Thrun - Stanford University
Learning Probabilistic Motion Models for Mobile Robots
[Abstract][Paper]
	Austin Eliazar - Duke University Ronald Parr - Duke University
Learning Random Walks for Inducing Word Dependency Distributions
[Abstract][Paper]
	Kristina Toutanova - Stanford University Christopher Manning - Stanford University Andrew Ng - Stanford University
Learning to Cluster using Local Neighborhood Structure
[Abstract][Paper]
	Romer Rosales - University of Toronto / MIT Kannan Achan - University of Toronto Brendan Frey - University of Toronto
Learning to Fly by Combining Reinforcement Learning with Behavioural Cloning
[Abstract][Paper]
	Eduardo Morales - Tec de Monterrey - Campus Cuernavaca Claude Sammut - University of New South Wales
Learning to Learn with the Informative Vector Machine
[Abstract][Paper]
	Neil Lawrence - University of Sheffield John Platt - Microsoft Research
Learning to Track 3D Human Motion from Silhouettes
[Abstract][Paper]
	Ankur Agarwal - GRAVIR - INRIA Rhone Alpes, Grenoble Bill Triggs - GRAVIR-INRIA-CNRS, Grenoble
Learning with Non Positive Kernels
[Abstract][Paper]
	Cheng Soon Ong - Australian National University Xavier Mary - ENSAE-CREST-LS Stephane Canu - INSA de Rouen Alexander Smola - Australian National University and NICTA
Leveraging the Margin More Carefully
[Abstract][Paper]
	Nir Krause - The Hebrew University of Jerusalem Yoram Singer - The Hebrew University of Jerusalem
Linearized Cluster Assignment via Spectral Ordering
[Abstract][Paper]
	Chris Ding - Lawrence Berkeley National Laboratory Xiaofeng He - Lawrence Berkeley National Laboratory
Links between Perceptrons, MLPs and SVMs
[Abstract][Paper]
	Ronan Collobert - IDIAP Samy Bengio - IDIAP
Locally Linear Metric Adaptation for Semi-Supervised Clustering
[Abstract][Paper]
	Hong Chang - Hong Kong University of Science and Technology Dit - Hong Kong University of Science and Technology
Lookahead Based Algorithms for Anytime Induction of Decision Trees
[Abstract][Paper]
	Saher Esmeir - Technion---Israel Institute of Technology Shaul Markovitch - Technion---Israel Institute of Technology
Margin Based Feature Selection - Theory and Algorithms
[Abstract][Paper]
	Ran Gilad - Hebrew University Amir Navot - Hebrew University Naftali Tishby - Hebrew University
Model Selection via the AUC
[Abstract][Paper]
	Saharon Rosset - IBM T.J. Watson Research Center
Multiple Kernel Learning, Conic Duality, and the SMO Algorithm
[Abstract][Paper]
	Francis Bach - UC Berkeley Gert Lanckriet - UC Berkeley Michael Jordan - UC Berkeley
Multi-Task Feature and Kernel Selection for SVMs
[Abstract][Paper]
	Tony Jebara - Columbia University
Nonparametric Classification with Polynomial MPMC Cascades
[Abstract][Paper]
	Sander Bohte - University of Colorado at Boulder / CWI Markus Breitenbach - University of Colorado at Boulder Gregory Grudic - University of Colorado at Boulder
Online Learning of Conditionally I.I.D. Data
[Abstract][Paper]
	Daniil Ryabko - Computer Learning Research Centre, Royal Holloway, University of London
Online Learning of Pseudo-Metrics
[Abstract][Paper]
	Shai Shalev - The Hebrew University Andrew Ng - Stanford University Yoram Singer - The Hebrew University
Optimising Area Under the ROC Curve Using Gradient Descent
[Abstract][Paper]
	Alan Herschtal - Telstra Corporation, Australia Bhavani Raskutti - Telstra Corporation, Australia
P3VI: A Partitioned, Prioritized, Parallel Value Iterator
[Abstract][Paper]
	David Wingate - Brigham Young University Kevin Seppi - Brigham Young University
Parameter Space Exploration With Gaussian Process Trees
[Abstract][Paper]
	Robert B. Gramacy - UC Santa Cruz Herbert K. H. Lee - UC Santa Cruz William G. MacReady - Research Institute for Advanced Computer Science / NASA AMES
Predictive Automatic Relevance Determination by Expectation Propagation
[Abstract][Paper]
	Yuan Qi - MIT Thomas Minka - Microsoft Research Rosalind Picard - MIT Zoubin Ghahramani - University Colledge London
Probabilistic Score Estimation with Piecewise Logistic Regression
[Abstract][Paper]
	Jian Zhang - Carnegie Mellon University Yiming Yang - Carnegie Mellon University
Probabilistic Tangent Subspace: A Unified View
[Abstract][Paper]
	Jianguo Lee - Department of Automation, Tsinghua University Jingdong Wang - Department of Automation, Tsinghua University Changshui Zhang - Department of Automation, Tsinghua University Zhaoqi Bian - Department of Automation, Tsinghua University
Redundant Feature Elimination for Multi-Class Problems
[Abstract][Paper]
	Annalisa Appice - University of Bari Michelangelo Ceci - University of Bari Simon Rawles - University of Bristol Peter Flach - University of Bristol
Relational Sequential Inference with Reliable Observations
[Abstract][Paper]
	Alan Fern - Purdue University Robert Givan - Purdue University
Robust Feature Induction for Support Vector Machines
[Abstract][Paper]
	Rong Jin - Michigan State University Huan Liu - Arizona State University
Semi-Supervised Learning Using Randomized Mincuts
[Abstract][Paper]
	Avrim Blum - Carnegie Mellon University John Lafferty - Carnegie Mellon University Mugizi Rwebangira - Carnegie Mellon University Rajashekar Reddy - Carnegie Mellon University
Sequential Information Bottleneck for Finite Data
[Abstract][Paper]
	Jaakko Peltonen - Helsinki University of Technology, Neural Networks Research Centre Janne Sinkkonen - Helsinki University of Technology, Neural Networks Research Centre Samuel Kaski - Helsinki University of Technology, Neural Networks Research Centre
Sequential Skewing: An Improved Skewing Algorithm
[Abstract][Paper]
	Soumya Ray - University of Wisconsin David Page - University of Wisconsin
Solving cluster ensemble problems by bipartite graph partitioning
[Abstract][Paper]
	Xiaoli Fern - Purdue University Carla Brodley - Purdue University
Solving Large Scale Linear Prediction Problems using Stochastic Gradient Descent Algorithms
[Abstract][Paper]
	Tong Zhang - IBM T.J. Watson Research Center
Sparse Cooperative Q-learning
[Abstract][Paper]
	Jelle Kok - University of Amsterdam Nikos Vlassis - University of Amsterdam
Support Vector Machine Learning for Interdependent and Structured Output Spaces
[Abstract][Paper]
	Ioannis Tsochantaridis - Brown University Thomas Hofmann - Brown University Thorsten Joachims - Cornell University Yasemin Altun - Brown University
Surrogate Maximization/Minimization Algorithms for AdaBoost and the Logistic Regression Model
[Abstract][Paper]
	Zhihua Zhang - Hong Kong University of Science and Technology James T. Kwok - Hong Kong University of Science and Technology Dit - Hong Kong University of Science and Technology
SVM-Based Generalized Multiple-Instance Learning via Approximate Box Counting
[Abstract][Paper]
	Qingping Tao - University of Nebraska Stephen Scott - University of Nebraska Vinodchandran Variyam - University of Nebraska Thomas Osugi - University of Nebraska
Take a Walk and Cluster Genes: A TSP-based Approach to Optimal Rearrangement Clustering
[Abstract][Paper]
	Sharlee Climer - Washington University in St. Louis Weixiong Zhang - Washington University in St. Louis
Testing the Significance of Attribute Interactions
[Abstract][Paper]
	Aleks Jakulin - University of Ljubljana Ivan Bratko - University of Ljubljana
Text Categorization with Many Redundant Features: Using Aggressive Feature Selection to Make SVMs Competitive with C4.5
[Abstract][Paper]
	Evgeniy Gabrilovich - Technion - Israel Institute of Technology Shaul Markovitch - Technion - Israel Institute of Technology
The Bayesian Backfitting Relevance Vector Machine
[Abstract][Paper]
	Aaron D'Souza - University of Southern California Sethu Vijayakumar - University of Edinburgh Stefan Schaal - University of Southern California & ATR Computational Neuroscience Laboratory
The Multiple Multiplicative Factor Model For Collaborative Filtering
[Abstract][Paper]
	Benjamin Marlin - University of Toronto Richard Zemel - University of Toronto
Towards Tight Bounds for Rule Learning
[Abstract][Paper]
	Ulrich Rückert - TU München Stefan Kramer - TU München
Tractable Learning of Large Bayes Net Structures from Sparse Data
[Abstract][Paper]
	Anna Goldenberg - Carnegie Mellon University Andrew Moore - Carnegie Mellon University
Training Conditional Random Fields via Gradient Tree Boosting
[Abstract][Paper]
	Thomas Dietterich - Oregon State University Adam Ashenfelter - Oregon State University and Cleverset, Inc. Yaroslav Bulatov - Oregon State University
Unifying Collaborative and Content-Based Filtering
[Abstract][Paper]
	Justin Basilico - Brown University Thomas Hofmann - Brown University
Using Relative Novelty to Identify Useful Temporal Abstractions in Reinforcement Learning
[Abstract][Paper]
	Ozgur Simsek - University of Massachusetts Amherst Andrew Barto - University of Massachusetts Amherst
Utile Distinction Hidden Markov Models
[Abstract][Paper]
	Daan Wierstra - Utrecht University Marco Wiering - Utrecht University
Variational methods for the Dirichlet process
[Abstract][Paper]
	David Blei - U. C. Berkeley Michael Jordan - U. C. Berkeley

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