17. ICML 2000:
Stanford,
CA,
USA
Pat Langley (Ed.):
Proceedings of the Seventeenth International Conference on Machine Learning (ICML 2000), Stanford University, Stanford, CA, USA, June 29 - July 2, 2000.
Morgan Kaufmann 2000, ISBN 1-55860-707-2 
- Ricardo Aler, Daniel Borrajo, Pedro Isasi:
Knowledge Representation Issues in Control Knowledge Learning.
1-8
- Erin L. Allwein, Robert E. Schapire, Yoram Singer:
Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers.
9-16
- Brigham S. Anderson, Andrew W. Moore, David Cohn:
A Nonparametric Approach to Noisy and Costly Optimization.
17-24
- Charles W. Anderson, Bruce A. Draper, David A. Peterson:
Behavioral Cloning of Student Pilots with Modular Neural Networks.
25-32
- Bikramjit Banerjee, Sandip Debnath, Sandip Sen:
Combining Multiple Perspectives.
33-40
- Jonathan Baxter, Peter L. Bartlett:
Reinforcement Learning in POMDP's via Direct Gradient Ascent.
41-48
- Stephen D. Bay, Michael J. Pazzani:
Characterizing Model Erros and Differences.
49-56
- Kristin P. Bennett, Erin J. Bredensteiner:
Duality and Geometry in SVM Classifiers.
57-64
- Kristin P. Bennett, Ayhan Demiriz, John Shawe-Taylor:
A Column Generation Algorithm For Boosting.
65-72
- Mihai Boicu, Gheorghe Tecuci, Dorin Marcu, Michael Bowman, Ping Shyr, Florin Ciucu, Cristian Levcovici:
Disciple-COA: From Agent Programming to Agent Teaching.
73-80
- Antony F. Bowers, Christophe G. Giraud-Carrier, John W. Lloyd:
Classification of Individuals with Complex Structure.
81-88
- Michael H. Bowling:
Convergence Problems of General-Sum Multiagent Reinforcement Learning.
89-94
- Matthew Brand:
Finding Variational Structure in Data by Cross-Entropy Optimization.
95-102
- Jake D. Brutlag, Christopher Meek:
Challenges of the Email Domain for Text Classification.
103-110
- Colin Campbell, Nello Cristianini, Alex J. Smola:
Query Learning with Large Margin Classifiers.
111-118
- William M. Campbell, Kari Torkkola, Sreeream V. Balakrishnan:
Dimension Reduction Techniques for Training Polynomial Networks.
119-126
- Huan Chang, David Cohn, Andrew McCallum:
Learning to Create Customized Authority Lists.
127-134
- Yong S. Choi, Suk I. Yoo:
Learning to Select Text Databases with Neural Nets.
135-142
- Eric Chown, Thomas G. Dietterich:
A Divide and Conquer Approach to Learning from Prior Knowledge.
143-150
- Jefferson A. Coelho Jr., Roderic A. Grupen:
Learning in Non-stationary Conditions: A Control Theoretic Approach.
151-158
- William W. Cohen:
Automatically Extracting Features for Concept Learning from the Web.
159-166
- David Cohn, Huan Chang:
Learning to Probabilistically Identify Authoritative Documents.
167-174
- Michael Collins:
Discriminative Reranking for Natural Language Parsing.
175-182
- Simon Colton, Alan Bundy, Toby Walsh:
Automatic Identification of Mathematical Concepts.
183-190
- Jörg Conradt, Gaurav Tevatia, Sethu Vijayakumar, Stefan Schaal:
On-line Learning for Humanoid Robot Systems.
191-198
- Mark Craven, David Page, Jude W. Shavlik, Joseph Bockhorst, Jeremy D. Glasner:
Using Multiple Levels of Learning and Diverse Evidence to Uncover Coordinately Controlled Genes.
199-206
- Daniela Pucci de Farias, Benjamin Van Roy:
Fixed Points of Approximate Value Iteration and Temporal-Difference Learning.
207-214
- Gerald DeJong:
Hidden Strengths and Limitations: An Empirical Investigation of Reinforcement Learning.
215-222
- Pedro Domingos:
Bayesian Averaging of Classifiers and the Overfitting Problem.
223-230
- Pedro Domingos:
A Unifeid Bias-Variance Decomposition and its Applications.
231-238
- Chris Drummond, Robert C. Holte:
Exploiting the Cost (In)sensitivity of Decision Tree Splitting Criteria.
239-246
- Jennifer G. Dy, Carla E. Brodley:
Feature Subset Selection and Order Identification for Unsupervised Learning.
247-254
- Eleazar Eskin:
Anomaly Detection over Noisy Data using Learned Probability Distributions.
255-262
- Floriana Esposito, Nicola Fanizzi, Stefano Ferilli, Giovanni Semeraro:
Ideal Theory Refinement under Object Identity.
263-270
- Theodoros Evgeniou, Luis Pérez-Breva, Massimiliano Pontil, Tomaso Poggio:
Bounds on the Generalization Performance of Kernel Machine Ensembles.
271-278
- Alan Fern, Robert Givan:
Online Ensemble Learning: An Empirical Study.
279-286
- Claude-Nicolas Fiechter, Seth Rogers:
Learning Subjective Functions with Large Margins.
287-294
- Jürgen Forster, Manfred K. Warmuth:
Relative Loss Bounds for Temporal-Difference Learning.
295-302
- Rayid Ghani:
Using Error-Correcting Codes for Text Classification.
303-310
- Attilio Giordana, Lorenza Saitta, Michèle Sebag, Marco Botta:
Analyzing Relational Learning in the Phase Transition Framework.
311-318
- Dani Goldberg, Maja J. Mataric:
Learning Multiple Models for Reward Maximization.
319-326
- Sally A. Goldman, Yan Zhou:
Enhancing Supervised Learning with Unlabeled Data.
327-334
- Geoffrey J. Gordon, Andrew Moore:
Learning Filaments.
335-342
- Gregory Z. Grudic, Lyle H. Ungar:
Localizing Policy Gradient Estimates to Action Transition.
343-350
- Keith Hall, Thomas Hofmann:
Learning Curved Multinomial Subfamilies for Natural Language Processing and Information Retrieval.
351-358
- Mark A. Hall:
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning.
359-366
- Tom Heskes:
Empirical Bayes for Learning to Learn.
367-374
- Véronique Hoste, Walter Daelemans, Erik F. Tjong Kim Sang, Steven Gillis:
Meta-Learning for Phonemic Annotation of Corpora.
375-382
- Dean F. Hougen, Maria L. Gini, James R. Slagle:
An Integrated Connectionist Approach to Reinforcement Learning for Robotic Control.
383-390
- Nicholas R. Howe:
Data as Ensembles of Records: Representation and Comparison.
391-398
- Chun-Nan Hsu, Hung-Ju Huang, Tzu-Tsung Wong:
Why Discretization Works for Naive Bayesian Classifiers.
399-406
- Junling Hu, Michael P. Wellman:
Experimental Results on Q-Learning for General-Sum Stochastic Games.
407-414
- Yi-Cheng Huang, Bart Selman, Henry A. Kautz:
Learning Declarative Control Rules for Constraint-BAsed Planning.
415-422
- Fan Jiang, Michael L. Littman:
Approximate Dimension Equalization in Vector-based Information Retrieval.
423-430
- Thorsten Joachims:
Estimating the Generalization Performance of an SVM Efficiently.
431-438
- Peter Ju, Leslie Pack Kaelbling, Yoram Singer:
State-based Classification of Finger Gestures from Electromyographic Signals.
439-446
- Susumu Katayama, Hajime Kimura, Shigenobu Kobayashi:
A Universal Generalization for Temporal-Difference Learning Using Haar Basis Functions.
447-454
- Cenk Kaynak, Ethem Alpaydin:
MultiStage Cascading of Multiple Classifiers: One Man's Noise is Another Man's Data.
455-462
- Jeffrey O. Kephart, Gerald Tesauro:
Pseudo-convergent Q-Learning by Competitive Pricebots.
463-470
- Roni Khardon:
Learning Horn Expressions with LogAn-H.
471-478
- Zu Whan Kim, Ramakant Nevatia:
Learning Bayesian Networks for Diverse and Varying numbers of Evidence Sets.
479-486
- Ralf Klinkenberg, Thorsten Joachims:
Detecting Concept Drift with Support Vector Machines.
487-494
- Paul Komarek, Andrew W. Moore:
A Dynamic Adaptation of AD-trees for Efficient Machine Learning on Large Data Sets.
495-502
- Miroslav Kubat, Martin Cooperson Jr.:
Voting Nearest-Neighbor Subclassifiers.
503-510
- Michail G. Lagoudakis, Michael L. Littman:
Algorithm Selection using Reinforcement Learning.
511-518
- Terran Lane, Carla E. Brodley:
Data Reduction Techniques for Instance-Based Learning from Human/Computer Interface Data.
519-526
- Tessa A. Lau, Pedro Domingos, Daniel S. Weld:
Version Space Algebra and its Application to Programming by Demonstration.
527-534
- Martin Lauer, Martin A. Riedmiller:
An Algorithm for Distributed Reinforcement Learning in Cooperative Multi-Agent Systems.
535-542
- Cen Li, Gautam Biswas:
A Bayesian Approach to Temporal Data Clustering using Hidden Markov Models.
543-550
- Jinyan Li, Kotagiri Ramamohanarao, Guozhu Dong:
The Space of Jumping Emerging Patterns and Its Incremental Maintenance Algorithms.
551-558
- Yi Li:
Selective Voting for Perception-like Online Learning.
559-566
- Marcus A. Maloof:
An Initial Study of an Adaptive Hierarchical Vision System.
567-574
- Hiroshi Mamitsuka, Naoki Abe:
Efficient Mining from Large Databases by Query Learning.
575-582
- Dragos D. Margineantu, Thomas G. Dietterich:
Bootstrap Methods for the Cost-Sensitive Evaluation of Classifiers.
583-590
- Andrew McCallum, Dayne Freitag, Fernando C. N. Pereira:
Maximum Entropy Markov Models for Information Extraction and Segmentation.
591-598
- Geoffrey J. McLachlan, David Peel:
Mixtures of Factor Analyzers.
599-606
- Andrew R. Mitchell:
"Boosting'' a Positive-Data-Only Learner.
607-614
- Robert Moll, Theodore J. Perkins, Andrew G. Barto:
Machine Learning for Subproblem Selection.
615-622
- Jun Morimoto, Kenji Doya:
Acquisition of Stand-up Behavior by a Real Robot using Hierarchical Reinforcement Learning.
623-630
- Stephen Muggleton, Christopher H. Bryant, Ashwin Srinivasan:
Learning Chomsky-like Grammars for Biological Sequence Families.
631-638
- Matthew D. Mullin, Rahul Sukthankar:
Complete Cross-Validation for Nearest Neighbor Classifiers.
639-646
- Rémi Munos, Andrew W. Moore:
Rates of Convergence for Variable Resolution Schemes in Optimal Control.
647-654
- Kary Myers, Michael J. Kearns, Satinder P. Singh, Marilyn A. Walker:
A Boosting Approach to Topic Spotting on Subdialogues.
655-662
- Andrew Y. Ng, Stuart J. Russell:
Algorithms for Inverse Reinforcement Learning.
663-670
- Daniel Nikovski, Illah R. Nourbakhsh:
Learning Probabilistic Models for Decision-Theoretic Navigation of Mobile Robots.
671-678
- Partha Niyogi, Narendra Karmarkar:
An Approach to Data Reduction and Clustering with Theoretical Guarantees.
679-686
- Tadashi Nomoto, Yuji Matsumoto:
Comparing the Minimum Description Length Principle and Boosting in the Automatic Analysis of Discourse.
687-694
- Seishi Okamoto, Nobuhiro Yugami:
Generalized Average-Case Analyses of the Nearest Neighbor Algorithm.
695-702
- Joseph O'Sullivan, John Langford, Rich Caruana, Avrim Blum:
FeatureBoost: A Meta-Learning Algorithm that Improves Model Robustness.
703-710
- Alberto Paccanaro, Geoffrey E. Hinton:
Learning Distributed Representations by Mapping Concepts and Relations into a Linear Space.
711-718
- Georgios Paliouras, Christos Papatheodorou, Vangelis Karkaletsis, Constantine D. Spyropoulos:
Clustering the Users of Large Web Sites into Communities.
719-726
- Dan Pelleg, Andrew W. Moore:
X-means: Extending K-means with Efficient Estimation of the Number of Clusters.
727-734
- David M. Pennock, Pedrito Maynard-Reid II, C. Lee Giles, Eric Horvitz:
A Normative Examination of Ensemble Learning Algorithms.
735-742
- Bernhard Pfahringer, Hilan Bensusan, Christophe G. Giraud-Carrier:
Meta-Learning by Landmarking Various Learning Algorithms.
743-750
- Justus H. Piater, Roderic A. Grupen:
Constructive Feature Learning and the Development of Visual Expertise.
751-758
- Doina Precup, Richard S. Sutton, Satinder P. Singh:
Eligibility Traces for Off-Policy Policy Evaluation.
759-766
- Jette Randløv:
Shaping in Reinforcement Learning by Changing the Physics of the Problem.
767-774
- Jette Randløv, Andrew G. Barto, Michael T. Rosenstein:
Combining Reinforcement Learning with a Local Control Algorithm.
775-782
- Stuart I. Reynolds:
Adaptive Resolution Model-Free Reinforcement Learning: Decision Boundary Partitioning.
783-790
- Corinna Richter, Jörg Stachowiak:
Knowledge Propagation in Model-based Reinforcement Learning Tasks.
791-798
- Charles R. Rosenberg:
Image Color Constancy Using EM and Cached Statistics.
799-806
- Malcolm R. K. Ryan, Mark D. Reid:
Learning to Fly: An Application of Hierarchical Reinforcement Learning.
807-814
- Matthias Rychetsky, John Shawe-Taylor, Manfred Glesner:
Direct Bayes Point Machines.
815-822
- Scott Sanner, John R. Anderson, Christian Lebiere, Marsha C. Lovett:
Achieving Efficient and Cognitively Plausible Learning in Backgammon.
823-830
- Tobias Scheffer:
Predicting the Generalization Performance of Cross Validatory Model Selection Criteria.
831-838
- Greg Schohn, David Cohn:
Less is More: Active Learning with Support Vector Machines.
839-846
- Dale Schuurmans, Finnegan Southey:
An Adaptive Regularization Criterion for Supervised Learning.
847-854
- Marc Sebban, Richard Nock:
Instance Pruning as an Information Preserving Problem.
855-862
- Richard Segal, Jeffrey O. Kephart:
Incremental Learning in SwiftFile.
863-870
- Thomas R. Shultz, François Rivest:
Using Knowledge to Speed Learning: A Comparison of Knowledge-based Cascade-correlation and Multi-task Learning.
871-878
- Ricardo Bezerra de Andrade e Silva, Teresa Bernarda Ludermir:
Obtaining Simplified Rule Bases by Hybrid Learning.
879-886
- Bryan Singer, Manuela M. Veloso:
Learning to Predict Performance from Formula Modeling and Training Data.
887-894
- Seán Slattery, Tom M. Mitchell:
Discovering Test Set Regularities in Relational Domains.
895-902
- William D. Smart, Leslie Pack Kaelbling:
Practical Reinforcement Learning in Continuous Spaces.
903-910
- Alex J. Smola, Bernhard Schölkopf:
Sparse Greedy Matrix Approximation for Machine Learning.
911-918
- Leen-Kiat Soh, Costas Tsatsoulis:
Using Learning by Discovery to Segment Remotely Sensed Images.
919-926
- Manu Sridharan, Gerald Tesauro:
Multi-agent Q-learning and Regression Trees for Automated Pricing Decisions.
927-934
- Peter Stone:
TPOT-RL Applied to Network Routing.
935-942
- Malcolm J. A. Strens:
A Bayesian Framework for Reinforcement Learning.
943-950
- Luis Talavera:
Feature Selection and Incremental Learning of Probabilistic Concept Hierarchies.
951-958
- Astro Teller, Manuela M. Veloso:
Efficient Learning Through Evolution: Neural Programming and Internal Reinforcement.
959-966
- Loo-Nin Teow, Kia-Fock Loe:
Selection of Support Vector Kernel Parameters for Improved Generalization.
967-974
- Franck Thollard, Pierre Dupont, Colin de la Higuera:
Probabilistic DFA Inference using Kullback-Leibler Divergence and Minimality.
975-982
- Kai Ming Ting:
A Comparative Study of Cost-Sensitive Boosting Algorithms.
983-990
- Ljupco Todorovski, Saso Dzeroski, Ashwin Srinivasan, Jonathan Whiteley, David Gavaghan:
Discovering the Structure of Partial Differential Equations from Example Behaviour.
991-998
- Simon Tong, Daphne Koller:
Support Vector Machine Active Learning with Application sto Text Classification.
999-1006
- Luís Torgo:
Partial Linear Trees.
1007-1014
- Kari Torkkola, William M. Campbell:
Mutual Information in Learning Feature Transformations.
1015-1022
- Geoffrey G. Towell:
Local Expert Autoassociators for Anomaly Detection.
1023-1030
- Geoffrey G. Towell, Thomas Petsche, Michael R. Miller:
Learning Priorities From Noisy Examples.
1031-1038
- Shivakumar Vaithyanathan, Byron Dom:
Hierarchical Unsupervised Learning.
1039-1046
- Tim Van Allen, Russell Greiner:
Model Selection Criteria for Learning Belief Nets: An Empirical Comparison.
1047-1054
- Antal van den Bosch, Jakub Zavrel:
Unpacking Multi-valued Symbolic Features and Classes in Memory-Based Language Learning.
1055-1062
- Menno van Zaanen:
Bootstrapping Syntax and Recursion using Alginment-Based Learning.
1063-1070
- Stefan Veeser:
An Evolutionary Approach to Evidence-Based Learning of Deterministic Finite Automata.
1071-1078
- Sethu Vijayakumar, Stefan Schaal:
Locally Weighted Projection Regression: Incremental Real Time Learning in High Dimensional Space.
1079-1086
- Ricardo Vilalta, Daniel Oblinger:
A Quantification of Distance Bias Between Evaluation Metrics In Classification.
1087-1094
- Slobodan Vucetic, Zoran Obradovic:
Discovering Homogeneous Regions in Spatial Data through Competition.
1095-1102
- Kiri Wagstaff, Claire Cardie:
Clustering with Instance-level Constraints.
1103-1110
- Marilyn A. Walker, Jeremy H. Wright, Irene Langkilde:
Using Natural Language Processing and discourse Features to Identify Understanding Errors.
1111-1118
- Jun Wang, Jean-Daniel Zucker:
Solving the Multiple-Instance Problem: A Lazy Learning Approach.
1119-1126
- Takashi Washio, Hiroshi Motoda, Yuji Niwa:
Enhancing the Plausibility of Law Equation Discovery.
1127-1134
- Sholom M. Weiss, Nitin Indurkhya:
Lightweight Rule Induction.
1135-1142
- Machiel Westerdijk, Wim Wiegerinck:
Classification with Multiple Latent Variable Models using Maximum Entropy Discrimination.
1143-1150
- Marco Wiering:
Multi-Agent Reinforcement Leraning for Traffic Light Control.
1151-1158
- Christopher K. I. Williams, Matthias Seeger:
The Effect of the Input Density Distribution on Kernel-based Classifiers.
1159-1166
- Yiming Yang, Tom Ault, Thomas Pierce:
Combining Multiple Learning Strategies for Effective Cross Validation.
1167-1174
- Olcay Taner Yildiz, Ethem Alpaydin:
Linear Discriminant Trees.
1175-1182
- Sarah Zelikovitz, Haym Hirsh:
Improving Short-Text Classification using Unlabeled Data for Classification Problems.
1191-1198
- Blaz Zupan, Ivan Bratko, Marko Bohanec, Janez Demsar:
Induction of Concept Hierarchies from Noisy Data.
1199-1206
- Pat Langley:
Crafting Papers on Machine Learning.
1207-1216
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