ICML 2004

117 papers

A Comparative Study on Methods for Reducing Myopia of Hill-Climbing Search in Multirelational Learning Lourdes Peña Castillo, Stefan Wrobel

A Fast Iterative Algorithm for Fisher Discriminant Using Heterogeneous Kernels Glenn Fung, Murat Dundar, Jinbo Bi, R. Bharat Rao

A Graphical Model for Protein Secondary Structure Prediction Wei Chu, Zoubin Ghahramani, David L. Wild

A Hierarchical Method for Multi-Class Support Vector Machines Volkan Vural, Jennifer G. Dy

A Kernel View of the Dimensionality Reduction of Manifolds Jihun Ham, Daniel D. Lee, Sebastian Mika, Bernhard Schölkopf

A Maximum Entropy Approach to Species Distribution Modeling Steven J. Phillips, Miroslav Dudík, Robert E. Schapire

A MFoM Learning Approach to Robust Multiclass Multi-Label Text Categorization Sheng Gao, Wen Wu, Chin-Hui Lee, Tat-Seng Chua

A Monte Carlo Analysis of Ensemble Classification Roberto Esposito, Lorenza Saitta

A Multiplicative Up-Propagation Algorithm Jong-Hoon Ahn, Seungjin Choi, Jong-Hoon Oh

A Needle in a Haystack: Local One-Class Optimization Koby Crammer, Gal Chechik

A Pitfall and Solution in Multi-Class Feature Selection for Text Classification George Forman

A Spatio-Temporal Extension to Isomap Nonlinear Dimension Reduction Odest Chadwicke Jenkins, Maja J. Mataric

A Theoretical Characterization of Linear SVM-Based Feature Selection Douglas P. Hardin, Ioannis Tsamardinos, Constantin F. Aliferis

Active Learning of Label Ranking Functions Klaus Brinker

Active Learning Using Pre-Clustering Hieu Tat Nguyen, Arnold W. M. Smeulders

Adaptive Cognitive Orthotics: Combining Reinforcement Learning and Constraint-Based Temporal Reasoning Matthew R. Rudary, Satinder Singh, Martha E. Pollack

An Information Theoretic Analysis of Maximum Likelihood Mixture Estimation for Exponential Families Arindam Banerjee, Inderjit S. Dhillon, Joydeep Ghosh, Srujana Merugu

Apprenticeship Learning via Inverse Reinforcement Learning Pieter Abbeel, Andrew Y. Ng

Approximate Inference by Markov Chains on Union Spaces Max Welling, Michal Rosen-Zvi, Yee Whye Teh

Authorship Verification as a One-Class Classification Problem Moshe Koppel, Jonathan Schler

Automated Hierarchical Mixtures of Probabilistic Principal Component Analyzers Ting Su, Jennifer G. Dy

Bayesian Haplo-Type Inference via the Dirichlet Process Eric P. Xing, Roded Sharan, Michael I. Jordan

Bayesian Inference for Transductive Learning of Kernel Matrix Using the Tanner-Wong Data Augmentation Algorithm Zhihua Zhang, Dit-Yan Yeung, James T. Kwok

Bellman Goes Relational Kristian Kersting, Martijn van Otterlo, Luc De Raedt

Bias and Variance in Value Function Estimation Shie Mannor, Duncan Simester, Peng Sun, John N. Tsitsiklis

Boosting Grammatical Inference with Confidence Oracles Jean-Christophe Janodet, Richard Nock, Marc Sebban, Henri-Maxime Suchier

Boosting Margin Based Distance Functions for Clustering Tomer Hertz, Aharon Bar-Hillel, Daphna Weinshall

C4.5 Competence mAP: A Phase Transition-Inspired Approach Nicolas Baskiotis, Michèle Sebag

Co-EM Support Vector Learning Ulf Brefeld, Tobias Scheffer

Coalition Calculation in a Dynamic Agent Environment Ted Scully, Michael G. Madden, Gerard Lyons

Communication Complexity as a Lower Bound for Learning in Games Vincent Conitzer, Tuomas Sandholm

Convergence of Synchronous Reinforcement Learning with Linear Function Approximation Artur Merke, Ralf Schoknecht

Decentralized Detection and Classification Using Kernel Methods XuanLong Nguyen, Martin J. Wainwright, Michael I. Jordan

Decision Trees with Minimal Costs Charles X. Ling, Qiang Yang, Jianning Wang, Shichao Zhang

Delegating Classifiers César Ferri, Peter A. Flach, José Hernández-Orallo

Distribution Kernels Based on Moments of Counts Corinna Cortes, Mehryar Mohri

Diverse Ensembles for Active Learning Prem Melville, Raymond J. Mooney

Dynamic Abstraction in Reinforcement Learning via Clustering Shie Mannor, Ishai Menache, Amit Hoze, Uri Klein

Dynamic Conditional Random Fields: Factorized Probabilistic Models for Labeling and Segmenting Sequence Data Charles Sutton, Khashayar Rohanimanesh, Andrew McCallum

Efficient Hierarchical MCMC for Policy Search Malcolm J. A. Strens

Ensemble Selection from Libraries of Models Rich Caruana, Alexandru Niculescu-Mizil, Geoff Crew, Alex Ksikes

Ensembles of Nested Dichotomies for Multi-Class Problems Eibe Frank, Stefan Kramer

Entropy-Based Criterion in Categorical Clustering Tao Li, Sheng Ma, Mitsunori Ogihara

Estimating Replicability of Classifier Learning Experiments Remco R. Bouckaert

Extensions of Marginalized Graph Kernels Pierre Mahé, Nobuhisa Ueda, Tatsuya Akutsu, Jean-Luc Perret, Jean-Philippe Vert

Feature Extraction via Generalized Uncorrelated Linear Discriminant Analysis Jieping Ye, Ravi Janardan, Qi Li, Haesun Park

Feature Subset Selection for Learning Preferences: A Case Study Antonio Bahamonde, Gustavo F. Bayón, Jorge Díez, José Ramón Quevedo, Oscar Luaces, Juan José del Coz, Jaime Alonso, Félix Goyache

Gaussian Process Classification for Segmenting and Annotating Sequences Yasemin Altun, Thomas Hofmann, Alexander J. Smola

Generalized Low Rank Approximations of Matrices Jieping Ye

Generative Modeling for Continuous Non-Linearly Embedded Visual Inference Cristian Sminchisescu, Allan D. Jepson

Gradient LASSO for Feature Selection Yongdai Kim, Jinseog Kim

Hyperplane Margin Classifiers on the Multinomial Manifold Guy Lebanon, John D. Lafferty

Improving SVM Accuracy by Training on Auxiliary Data Sources Pengcheng Wu, Thomas G. Dietterich

Incremental Learning of Linear Model Trees Duncan Potts

Integrating Constraints and Metric Learning in Semi-Supervised Clustering Mikhail Bilenko, Sugato Basu, Raymond J. Mooney

Interpolation-Based Q-Learning Csaba Szepesvári, William D. Smart

K-Means Clustering via Principal Component Analysis Chris H. Q. Ding, Xiaofeng He

Kernel Conditional Random Fields: Representation and Clique Selection John D. Lafferty, Xiaojin Zhu, Yan Liu

Kernel-Based Discriminative Learning Algorithms for Labeling Sequences, Trees, and Graphs Hisashi Kashima, Yuta Tsuboi

Large Margin Hierarchical Classification Ofer Dekel, Joseph Keshet, Yoram Singer

Learning a Kernel Matrix for Nonlinear Dimensionality Reduction Kilian Q. Weinberger, Fei Sha, Lawrence K. Saul

Learning and Discovery of Predictive State Representations in Dynamical Systems with Reset Michael R. James, Satinder Singh

Learning and Evaluating Classifiers Under Sample Selection Bias Bianca Zadrozny

Learning Associative Markov Networks Benjamin Taskar, Vassil Chatalbashev, Daphne Koller

Learning Bayesian Network Classifiers by Maximizing Conditional Likelihood Daniel Grossman, Pedro M. Domingos

Learning First-Order Rules from Data with Multiple Parts: Applications on Mining Chemical Compound Data Cholwich Nattee, Sukree Sinthupinyo, Masayuki Numao, Takashi Okada

Learning Large Margin Classifiers Locally and Globally Kaizhu Huang, Haiqin Yang, Irwin King, Michael R. Lyu

Learning Low Dimensional Predictive Representations Matthew Rosencrantz, Geoffrey J. Gordon, Sebastian Thrun

Learning Probabilistic Motion Models for Mobile Robots Austin I. Eliazar, Ronald Parr

Learning Random Walk Models for Inducing Word Dependency Distributions Kristina Toutanova, Christopher D. Manning, Andrew Y. Ng

Learning to Cluster Using Local Neighborhood Structure Rómer Rosales, Kannan Achan, Brendan J. Frey

Learning to Fly by Combining Reinforcement Learning with Behavioural Cloning Eduardo F. Morales, Claude Sammut

Learning to Learn with the Informative Vector Machine Neil D. Lawrence, John C. Platt

Learning to Track 3D Human Motion from Silhouettes Ankur Agarwal, Bill Triggs

Learning with Non-Positive Kernels Cheng Soon Ong, Xavier Mary, Stéphane Canu, Alexander J. Smola

Leveraging the Margin More Carefully Nir Krause, Yoram Singer

Linearized Cluster Assignment via Spectral Ordering Chris H. Q. Ding, Xiaofeng He

Links Between Perceptrons, MLPs and SVMs Ronan Collobert, Samy Bengio

Locally Linear Metric Adaptation for Semi-Supervised Clustering Hong Chang, Dit-Yan Yeung

Lookahead-Based Algorithms for Anytime Induction of Decision Trees Saher Esmeir, Shaul Markovitch

Margin Based Feature Selection - Theory and Algorithms Ran Gilad-Bachrach, Amir Navot, Naftali Tishby

Model Selection via the AUC Saharon Rosset

Multi-Task Feature and Kernel Selection for SVMs Tony Jebara

Multiple Kernel Learning, Conic Duality, and the SMO Algorithm Francis R. Bach, Gert R. G. Lanckriet, Michael I. Jordan

Nonparametric Classification with Polynomial MPMC Cascades Sander M. Bohté, Markus Breitenbach, Gregory Z. Grudic

Online and Batch Learning of Pseudo-Metrics Shai Shalev-Shwartz, Yoram Singer, Andrew Y. Ng

Online Learning of Conditionally I.I.D. Data Daniil Ryabko

Optimising Area Under the ROC Curve Using Gradient Descent Alan Herschtal, Bhavani Raskutti

P3VI: A Partitioned, Prioritized, Parallel Value Iterator David Wingate, Kevin D. Seppi

Parameter Space Exploration with Gaussian Process Trees Robert B. Gramacy, Herbert K. H. Lee, William G. Macready

Predictive Automatic Relevance Determination by Expectation Propagation Yuan (Alan) Qi, Thomas P. Minka, Rosalind W. Picard, Zoubin Ghahramani

Probabilistic Score Estimation with Piecewise Logistic Regression Jian Zhang, Yiming Yang

Probabilistic Tangent Subspace: A Unified View Jianguo Lee, Jingdong Wang, Changshui Zhang, Zhaoqi Bian

Redundant Feature Elimination for Multi-Class Problems Annalisa Appice, Michelangelo Ceci, Simon Alan Rawles, Peter A. Flach

Relational Sequential Inference with Reliable Observations Alan Fern, Robert Givan

Robust Feature Induction for Support Vector Machines Rong Jin, Huan Liu

Semi-Supervised Learning Using Randomized Mincuts Avrim Blum, John D. Lafferty, Mugizi Robert Rwebangira, Rajashekar Reddy

Sequential Information Bottleneck for Finite Data Jaakko Peltonen, Janne Sinkkonen, Samuel Kaski

Sequential Skewing: An Improved Skewing Algorithm Soumya Ray, David Page

Solving Cluster Ensemble Problems by Bipartite Graph Partitioning Xiaoli Zhang Fern, Carla E. Brodley

Solving Large Scale Linear Prediction Problems Using Stochastic Gradient Descent Algorithms Tong Zhang

Sparse Cooperative Q-Learning Jelle R. Kok, Nikos Vlassis

Support Vector Machine Learning for Interdependent and Structured Output Spaces Ioannis Tsochantaridis, Thomas Hofmann, Thorsten Joachims, Yasemin Altun

Surrogate Maximization/minimization Algorithms for AdaBoost and the Logistic Regression Model Zhihua Zhang, James T. Kwok, Dit-Yan Yeung

SVM-Based Generalized Multiple-Instance Learning via Approximate Box Counting Qingping Tao, Stephen Donald Scott, N. V. Vinodchandran, Thomas Takeo Osugi

Take a Walk and Cluster Genes: A TSP-Based Approach to Optimal Rearrangement Clustering Sharlee Climer, Weixiong Zhang

Testing the Significance of Attribute Interactions Aleks Jakulin, Ivan Bratko

Text Categorization with Many Redundant Features: Using Aggressive Feature Selection to Make SVMs Competitive with C4.5 Evgeniy Gabrilovich, Shaul Markovitch

The Bayesian Backfitting Relevance Vector Machine Aaron D'Souza, Sethu Vijayakumar, Stefan Schaal

The Multiple Multiplicative Factor Model for Collaborative Filtering Benjamin M. Marlin, Richard S. Zemel

Towards Tight Bounds for Rule Learning Ulrich Rückert, Stefan Kramer

Tractable Learning of Large Bayes Net Structures from Sparse Data Anna Goldenberg, Andrew W. Moore

Training Conditional Random Fields via Gradient Tree Boosting Thomas G. Dietterich, Adam Ashenfelter, Yaroslav Bulatov

Unifying Collaborative and Content-Based Filtering Justin Basilico, Thomas Hofmann

Using Relative Novelty to Identify Useful Temporal Abstractions in Reinforcement Learning Özgür Simsek, Andrew G. Barto

Utile Distinction Hidden Markov Models Daan Wierstra, Marco A. Wiering

Variational Methods for the Dirichlet Process David M. Blei, Michael I. Jordan