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
- Jonathan Baxter, Peter L. Bartlett: Reinforcement Learning in POMDP's via Direct Gradient Ascent. 41-48
- 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
- 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
- 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
- Michael Collins: Discriminative Reranking for Natural Language Parsing. 175-182
- 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
- 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
- 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
- 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
- Paul Komarek, Andrew W. Moore: A Dynamic Adaptation of AD-trees for Efficient Machine Learning on Large Data Sets. 495-502
- 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
- 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
- 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
- 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
- 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
- Dale Schuurmans, Finnegan Southey: An Adaptive Regularization Criterion for Supervised Learning. 847-854
- 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
- William D. Smart, Leslie Pack Kaelbling: Practical Reinforcement Learning in Continuous Spaces. 903-910
- 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 P. 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
- 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
- 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
- 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
- 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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