ICML 2000

151 papers

"Boosting'' a Positive-Data-Only Learner Andrew R. Mitchell

A Bayesian Approach to Temporal Data Clustering Using Hidden Markov Models Cen Li, Gautam Biswas

A Bayesian Framework for Reinforcement Learning Malcolm J. A. Strens

A Boosting Approach to Topic Spotting on Subdialogues Kary L. Myers, Michael J. Kearns, Satinder Singh, Marilyn A. Walker

A Column Generation Algorithm for Boosting Kristin P. Bennett, Ayhan Demiriz, John Shawe-Taylor

A Comparative Study of Cost-Sensitive Boosting Algorithms Kai Ming Ting

A Divide and Conquer Approach to Learning from Prior Knowledge Eric Chown, Thomas G. Dietterich

A Dynamic Adaptation of AD-Trees for Efficient Machine Learning on Large Data Sets Paul Komarek, Andrew W. Moore

A Nonparametric Approach to Noisy and Costly Optimization Brigham S. Anderson, Andrew W. Moore, David Cohn

A Normative Examination of Ensemble Learning Algorithms David M. Pennock, Pedrito Maynard-Reid Ii, C. Lee Giles, Eric Horvitz

A Quantification of Distance Bias Between Evaluation Metrics in Classification Ricardo Vilalta, Daniel Oblinger

A Unifeid Bias-Variance Decomposition and Its Applications Pedro M. Domingos

A Universal Generalization for Temporal-Difference Learning Using Haar Basis Functions Susumu Katayama, Hajime Kimura, Shigenobu Kobayashi

Achieving Efficient and Cognitively Plausible Learning in Backgammon Scott Sanner, John R. Anderson, Christian Lebiere, Marsha C. Lovett

Acquisition of Stand-up Behavior by a Real Robot Using Hierarchical Reinforcement Learning Jun Morimoto, Kenji Doya

Adaptive Resolution Model-Free Reinforcement Learning: Decision Boundary Partitioning Stuart I. Reynolds

Algorithm Selection Using Reinforcement Learning Michail G. Lagoudakis, Michael L. Littman

Algorithms for Inverse Reinforcement Learning Andrew Y. Ng, Stuart Russell

An Adaptive Regularization Criterion for Supervised Learning Dale Schuurmans, Finnegan Southey

An Algorithm for Distributed Reinforcement Learning in Cooperative Multi-Agent Systems Martin Lauer, Martin A. Riedmiller

An Approach to Data Reduction and Clustering with Theoretical Guarantees Partha Niyogi, Narendra Karmarkar

An Evolutionary Approach to Evidence-Based Learning of Deterministic Finite Automata Stefan Veeser

An Initial Study of an Adaptive Hierarchical Vision System Marcus A. Maloof

An Integrated Connectionist Approach to Reinforcement Learning for Robotic Control Dean F. Hougen, Maria L. Gini, James R. Slagle

Analyzing Relational Learning in the Phase Transition Framework Attilio Giordana, Lorenza Saitta, Michèle Sebag, Marco Botta

Anomaly Detection over Noisy Data Using Learned Probability Distributions Eleazar Eskin

Approximate Dimension Equalization in Vector-Based Information Retrieval Fan Jiang, Michael L. Littman

Automatic Identification of Mathematical Concepts Simon Colton, Alan Bundy, Toby Walsh

Automatically Extracting Features for Concept Learning from the Web William W. Cohen

Bayesian Averaging of Classifiers and the Overfitting Problem Pedro M. Domingos

Behavioral Cloning of Student Pilots with Modular Neural Networks Charles W. Anderson, Bruce A. Draper, David A. Peterson

Bootstrap Methods for the Cost-Sensitive Evaluation of Classifiers Dragos D. Margineantu, Thomas G. Dietterich

Bootstrapping Syntax and Recursion Using Alginment-Based Learning Menno van Zaanen

Bounds on the Generalization Performance of Kernel Machine Ensembles Theodoros Evgeniou, Luis Pérez-Breva, Massimiliano Pontil, Tomaso A. Poggio

Challenges of the Email Domain for Text Classification Jake D. Brutlag, Christopher Meek

Characterizing Model Erros and Differences Stephen D. Bay, Michael J. Pazzani

Classification of Individuals with Complex Structure Antony Francis Bowers, Christophe G. Giraud-Carrier, John W. Lloyd

Classification with Multiple Latent Variable Models Using Maximum Entropy Discrimination Machiel Westerdijk, Wim Wiegerinck

Clustering the Users of Large Web Sites into Communities Georgios Paliouras, Christos Papatheodorou, Vangelis Karkaletsis, Constantine D. Spyropoulos

Clustering with Instance-Level Constraints Kiri Wagstaff, Claire Cardie

Combining Multiple Learning Strategies for Effective Cross Validation Yiming Yang, Tom Ault, Thomas Pierce

Combining Multiple Perspectives Bikramjit Banerjee, Sandip Debnath, Sandip Sen

Combining Reinforcement Learning with a Local Control Algorithm Jette Randløv, Andrew G. Barto, Michael T. Rosenstein

Comparing the Minimum Description Length Principle and Boosting in the Automatic Analysis of Discourse Tadashi Nomoto, Yuji Matsumoto

Complete Cross-Validation for Nearest Neighbor Classifiers Matthew D. Mullin, Rahul Sukthankar

Constructive Feature Learning and the Development of Visual Expertise Justus H. Piater, Roderic A. Grupen

Convergence Problems of General-Sum Multiagent Reinforcement Learning Michael H. Bowling

Correlation-Based Feature Selection for Discrete and Numeric Class Machine Learning Mark A. Hall

Crafting Papers on Machine Learning Pat Langley

Data as Ensembles of Records: Representation and Comparison Nicholas R. Howe

Data Reduction Techniques for Instance-Based Learning from Human/Computer Interface Data Terran Lane, Carla E. Brodley

Detecting Concept Drift with Support Vector Machines Ralf Klinkenberg, Thorsten Joachims

Dimension Reduction Techniques for Training Polynomial Networks William M. Campbell, Kari Torkkola, Sreeream V. Balakrishnan

Direct Bayes Point Machines Matthias Rychetsky, John Shawe-Taylor, Manfred Glesner

Disciple-COA: From Agent Programming to Agent Teaching Mihai Boicu, Gheorghe Tecuci, Dorin Marcu, Michael Bowman, Ping Shyr, Florin Ciucu, Cristian Levcovici

Discovering Homogeneous Regions in Spatial Data Through Competition Slobodan Vucetic, Zoran Obradovic

Discovering Test Set Regularities in Relational Domains Seán Slattery, Tom M. Mitchell

Discovering the Structure of Partial Differential Equations from Example Behaviour Ljupco Todorovski, Saso Dzeroski, Ashwin Srinivasan, Jonathan P. Whiteley, David Gavaghan

Discriminative Reranking for Natural Language Parsing Michael Collins

Duality and Geometry in SVM Classifiers Kristin P. Bennett, Erin J. Bredensteiner

Efficient Learning Through Evolution: Neural Programming and Internal Reinforcement Astro Teller, Manuela M. Veloso

Efficient Mining from Large Databases by Query Learning Hiroshi Mamitsuka, Naoki Abe

Eligibility Traces for Off-Policy Policy Evaluation Doina Precup, Richard S. Sutton, Satinder Singh

Empirical Bayes for Learning to Learn Tom Heskes

Enhancing Supervised Learning with Unlabeled Data Sally A. Goldman, Yan Zhou

Enhancing the Plausibility of Law Equation Discovery Takashi Washio, Hiroshi Motoda, Yuji Niwa

Estimating the Generalization Performance of an SVM Efficiently Thorsten Joachims

Experimental Results on Q-Learning for General-Sum Stochastic Games Junling Hu, Michael P. Wellman

Exploiting the Cost (In)sensitivity of Decision Tree Splitting Criteria Chris Drummond, Robert C. Holte

Feature Selection and Incremental Learning of Probabilistic Concept Hierarchies Luis Talavera

Feature Subset Selection and Order Identification for Unsupervised Learning Jennifer G. Dy, Carla E. Brodley

FeatureBoost: A Meta-Learning Algorithm That Improves Model Robustness Joseph O'Sullivan, John Langford, Rich Caruana, Avrim Blum

Finding Variational Structure in Data by Cross-Entropy Optimization Matthew Brand

Fixed Points of Approximate Value Iteration and Temporal-Difference Learning Daniela Pucci de Farias, Benjamin Van Roy

Generalized Average-Case Analyses of the Nearest Neighbor Algorithm Seishi Okamoto, Nobuhiro Yugami

Hidden Strengths and Limitations: An Empirical Investigation of Reinforcement Learning Gerald DeJong

Hierarchical Unsupervised Learning Shivakumar Vaithyanathan, Byron Dom

Ideal Theory Refinement Under Object Identity Floriana Esposito, Nicola Fanizzi, Stefano Ferilli, Giovanni Semeraro

Image Color Constancy Using EM and Cached Statistics Charles R. Rosenberg

Improving Short-Text Classification Using Unlabeled Data for Classification Problems Sarah Zelikovitz, Haym Hirsh

Incremental Learning in SwiftFile Richard B. Segal, Jeffrey O. Kephart

Induction of Concept Hierarchies from Noisy Data Blaz Zupan, Ivan Bratko, Marko Bohanec, Janez Demsar

Instance Pruning as an Information Preserving Problem Marc Sebban, Richard Nock

Knowledge Propagation in Model-Based Reinforcement Learning Tasks Corinna Richter, Jörg Stachowiak

Knowledge Representation Issues in Control Knowledge Learning Ricardo Aler, Daniel Borrajo, Pedro Isasi

Learning Bayesian Networks for Diverse and Varying Numbers of Evidence Sets Zu Whan Kim, Ramakant Nevatia

Learning Chomsky-like Grammars for Biological Sequence Families Stephen H. Muggleton, Christopher H. Bryant, Ashwin Srinivasan

Learning Curved Multinomial Subfamilies for Natural Language Processing and Information Retrieval Keith B. Hall, Thomas Hofmann

Learning Declarative Control Rules for Constraint-BAsed Planning Yi-Cheng Huang, Bart Selman, Henry A. Kautz

Learning Distributed Representations by Mapping Concepts and Relations into a Linear Space Alberto Paccanaro, Geoffrey E. Hinton

Learning Filaments Geoffrey J. Gordon, Andrew W. Moore

Learning Horn Expressions with LogAn-H Roni Khardon

Learning in Non-Stationary Conditions: A Control Theoretic Approach Jefferson A. Coelho Jr., Roderic A. Grupen

Learning Multiple Models for Reward Maximization Dani Goldberg, Maja J. Mataric

Learning Priorities from Noisy Examples Geoffrey G. Towell, Thomas Petsche, Michael R. Miller

Learning Probabilistic Models for Decision-Theoretic Navigation of Mobile Robots Daniel Nikovski, Illah R. Nourbakhsh

Learning Subjective Functions with Large Margins Claude-Nicolas Fiechter, Seth Rogers

Learning to Create Customized Authority Lists Huan Chang, David Cohn, Andrew McCallum

Learning to Fly: An Application of Hierarchical Reinforcement Learning Malcolm Ryan, Mark D. Reid

Learning to Predict Performance from Formula Modeling and Training Data Bryan Singer, Manuela M. Veloso

Learning to Probabilistically Identify Authoritative Documents David Cohn, Huan Chang

Learning to Select Text Databases with Neural Nets Yong S. Choi, Suk I. Yoo

Less Is More: Active Learning with Support Vector Machines Greg Schohn, David Cohn

Lightweight Rule Induction Sholom M. Weiss, Nitin Indurkhya

Linear Discriminant Trees Olcay Taner Yildiz, Ethem Alpaydin

Local Expert Autoassociators for Anomaly Detection Geoffrey G. Towell

Localizing Policy Gradient Estimates to Action Transition Gregory Z. Grudic, Lyle H. Ungar

Locally Weighted Projection Regression: Incremental Real Time Learning in High Dimensional Space Sethu Vijayakumar, Stefan Schaal

Machine Learning for Subproblem Selection Robert Moll, Theodore J. Perkins, Andrew G. Barto

Maximum Entropy Markov Models for Information Extraction and Segmentation Andrew McCallum, Dayne Freitag, Fernando C. N. Pereira

Meta-Learning by Landmarking Various Learning Algorithms Bernhard Pfahringer, Hilan Bensusan, Christophe G. Giraud-Carrier

Meta-Learning for Phonemic Annotation of Corpora Véronique Hoste, Walter Daelemans, Erik F. Tjong Kim Sang, Steven Gillis

Mixtures of Factor Analyzers Geoffrey J. McLachlan, David Peel

Model Selection Criteria for Learning Belief Nets: An Empirical Comparison Tim Van Allen, Russell Greiner

Multi-Agent Q-Learning and Regression Trees for Automated Pricing Decisions Manu Sridharan, Gerald Tesauro

Multi-Agent Reinforcement Leraning for Traffic Light Control Marco A. Wiering

MultiStage Cascading of Multiple Classifiers: One Man's Noise Is Another Man's Data Cenk Kaynak, Ethem Alpaydin

Mutual Information in Learning Feature Transformations Kari Torkkola, William M. Campbell

Obtaining Simplified Rule Bases by Hybrid Learning Ricardo Bezerra de Andrade e Silva, Teresa Bernarda Ludermir

On-Line Learning for Humanoid Robot Systems Jörg Conradt, Gaurav Tevatia, Sethu Vijayakumar, Stefan Schaal

Online Ensemble Learning: An Empirical Study Alan Fern, Robert Givan

Partial Linear Trees Luís Torgo

Practical Reinforcement Learning in Continuous Spaces William D. Smart, Leslie Pack Kaelbling

Predicting the Generalization Performance of Cross Validatory Model Selection Criteria Tobias Scheffer

Probabilistic DFA Inference Using Kullback-Leibler Divergence and Minimality Franck Thollard, Pierre Dupont, Colin de la Higuera

Pseudo-Convergent Q-Learning by Competitive Pricebots Jeffrey O. Kephart, Gerald Tesauro

Query Learning with Large Margin Classifiers Colin Campbell, Nello Cristianini, Alexander J. Smola

Rates of Convergence for Variable Resolution Schemes in Optimal Control Rémi Munos, Andrew W. Moore

Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers Erin L. Allwein, Robert E. Schapire, Yoram Singer

Reinforcement Learning in POMDP's via Direct Gradient Ascent Jonathan Baxter, Peter L. Bartlett

Relative Loss Bounds for Temporal-Difference Learning Jürgen Forster, Manfred K. Warmuth

Selection of Support Vector Kernel Parameters for Improved Generalization Loo-Nin Teow, Kia-Fock Loe

Selective Voting for Perception-like Online Learning Yi Li

Shaping in Reinforcement Learning by Changing the Physics of the Problem Jette Randløv

Solving the Multiple-Instance Problem: A Lazy Learning Approach Jun Wang, Jean-Daniel Zucker

Sparse Greedy Matrix Approximation for Machine Learning Alexander J. Smola, Bernhard Schölkopf

State-Based Classification of Finger Gestures from Electromyographic Signals Peter Ju, Leslie Pack Kaelbling, Yoram Singer

Support Vector Machine Active Learning with Application Sto Text Classification Simon Tong, Daphne Koller

The Effect of the Input Density Distribution on Kernel-Based Classifiers Christopher K. I. Williams, Matthias W. Seeger

The Space of Jumping Emerging Patterns and Its Incremental Maintenance Algorithms Jinyan Li, Kotagiri Ramamohanarao, Guozhu Dong

TPOT-RL Applied to Network Routing Peter Stone

Unpacking Multi-Valued Symbolic Features and Classes in Memory-Based Language Learning Antal van den Bosch, Jakub Zavrel

Using Error-Correcting Codes for Text Classification Rayid Ghani

Using Knowledge to Speed Learning: A Comparison of Knowledge-Based Cascade-Correlation and Multi-Task Learning Thomas R. Shultz, François Rivest

Using Learning by Discovery to Segment Remotely Sensed Images Leen-Kiat Soh, Costas Tsatsoulis

Using Multiple Levels of Learning and Diverse Evidence to Uncover Coordinately Controlled Genes Mark W. Craven, David Page, Jude W. Shavlik, Joseph Bockhorst, Jeremy D. Glasner

Using Natural Language Processing and Discourse Features to Identify Understanding Errors Marilyn A. Walker, Jeremy H. Wright, Irene Langkilde

Version Space Algebra and Its Application to Programming by Demonstration Tessa A. Lau, Pedro M. Domingos, Daniel S. Weld

Voting Nearest-Neighbor Subclassifiers Miroslav Kubat, Martin Cooperson Jr.

Why Discretization Works for Naive Bayesian Classifiers Chun-Nan Hsu, Hung-Ju Huang, Tzu-Tsung Wong

X-Means: Extending K-Means with Efficient Estimation of the Number of Clusters Dan Pelleg, Andrew W. Moore