18. ICML 2001:
Williams College, Williamstown, MA, USA
Carla E. Brodley, Andrea Pohoreckyj Danyluk (Eds.):
Proceedings of the Eighteenth International Conference on Machine Learning (ICML 2001), Williams College, Williamstown, MA, USA, June 28 - July 1, 2001.
Morgan Kaufmann 2001, ISBN 1-55860-778-1 
- Robert A. Amar, Daniel R. Dooly, Sally A. Goldman, Qi Zhang:
Multiple-Instance Learning of Real-Valued Data.
3-10
- Hendrik Blockeel, Jan Struyf:
Efficient algorithms for decision tree cross-validation.
11-18
- Avrim Blum, Shuchi Chawla:
Learning from Labeled and Unlabeled Data using Graph Mincuts.
19-26
- Michael H. Bowling, Manuela M. Veloso:
Convergence of Gradient Dynamics with a Variable Learning Rate.
27-34
- Urszula Chajewska, Daphne Koller, Dirk Ormoneit:
Learning an Agent's Utility Function by Observing Behavior.
35-42
- David Choi, Benjamin Van Roy:
A Generalized Kalman Filter for Fixed Point Approximation and Efficient Temporal Difference Learning.
43-50
- Wei Chu, S. Sathiya Keerthi, Chong Jin Ong:
A Unified Loss Function in Bayesian Framework for Support Vector Regression.
51-58
- Craig W. Codrington:
Boosting with Confidence Information.
59-65
- Nello Cristianini, John Shawe-Taylor, Huma Lodhi:
Latent Semantic Kernels.
66-73
- Sanmay Das:
Filters, Wrappers and a Boosting-Based Hybrid for Feature Selection.
74-81
- Richard Dearden:
Structured Prioritised Sweeping.
82-89
- Alin Dobra, Johannes Gehrke:
Bias Correction in Classification Tree Construction.
90-97
- Carlotta Domeniconi, Dimitrios Gunopulos:
An Efficient Approach for Approximating Multi-dimensional Range Queries and Nearest Neighbor Classification in Large Datasets.
98-105
- Pedro Domingos, Geoff Hulten:
A General Method for Scaling Up Machine Learning Algorithms and its Application to Clustering.
106-113
- Melissa Dominguez, Robert A. Jacobs:
Visual Development and the Acquisition of Binocular Disparity Sensitivities.
114-121
- Harris Drucker, Behzad Shahraray, David C. Gibbon:
Relevance Feedback using Support Vector Machines.
122-129
- Tina Eliassi-Rad, Jude W. Shavlik:
A Theory-Refinement Approach to Information Extraction.
130-137
- Yaakov Engel, Shie Mannor:
Learning Embedded Maps of Markov Processes.
138-145
- Johannes Fürnkranz:
Round Robin Rule Learning.
146-153
- Thomas Gärtner, Peter A. Flach:
WBCsvm: Weighted Bayesian Classification based on Support Vector Machines.
154-161
- Peter Geibel:
Reinforcement Learning with Bounded Risk.
162-169
- Lise Getoor, Nir Friedman, Daphne Koller, Benjamin Taskar:
Learning Probabilistic Models of Relational Structure.
170-177
- Rayid Ghani, Seán Slattery, Yiming Yang:
Hypertext Categorization using Hyperlink Patterns and Meta Data.
178-185
- Mohammad Ghavamzadeh, Sridhar Mahadevan:
Continuous-Time Hierarchical Reinforcement Learning.
186-193
- Matthew R. Glickman, Katia P. Sycara:
Evolutionary Search, Stochastic Policies with Memory, and Reinforcement Learning with Hidden State.
194-201
- Greg Hamerly, Charles Elkan:
Bayesian approaches to failure prediction for disk drives.
202-209
- Marcus Hutter:
General Loss Bounds for Universal Sequence Prediction.
210-217
- Yuri A. Ivanov, Bruce Blumberg, Alex Pentland:
Expectation Maximization for Weakly Labeled Data.
218-225
- Amir Jafari, Amy Greenwald, David Gondek, Gunes Ercal:
On No-Regret Learning, Fictitious Play, and Nash Equilibrium.
226-233
- Wenxin Jiang:
Some Theoretical Aspects of Boosting in the Presence of Noisy Data.
234-241
- Rong Jin, Alexander G. Hauptmann:
Learning to Select Good Title Words: An New Approach based on Reverse Information Retrieval.
242-249
- Thorsten Joachims, Nello Cristianini, John Shawe-Taylor:
Composite Kernels for Hypertext Categorisation.
250-257
- Stefan Kramer, Luc De Raedt:
Feature Construction with Version Spaces for Biochemical Applications.
258-265
- Krzysztof Krawiec:
Pairwise Comparison of Hypotheses in Evolutionary Learning.
266-273
- Abba Krieger, Chuan Long, Abraham Wyner:
Boosting Noisy Data.
274-281
- John D. Lafferty, Andrew McCallum, Fernando C. N. Pereira:
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data.
282-289
- John Langford, Matthias Seeger, Nimrod Megiddo:
An Improved Predictive Accuracy Bound for Averaging Classifiers.
290-297
- Patrice Latinne, Marco Saerens, Christine Decaestecker:
Adjusting the Outputs of a Classifier to New a Priori Probabilities May Significantly Improve Classification Accuracy: Evidence from a multi-class problem in remote sensing.
298-305
- Neil D. Lawrence, Bernhard Schölkopf:
Estimating a Kernel Fisher Discriminant in the Presence of Label Noise.
306-313
- Wee Sun Lee:
Collaborative Learning and Recommender Systems.
314-321
- Michael L. Littman:
Friend-or-Foe Q-learning in General-Sum Games.
322-328
- Yufeng Liu, Rosemary Emery, Deepayan Chakrabarti, Wolfram Burgard, Sebastian Thrun:
Using EM to Learn 3D Models of Indoor Environments with Mobile Robots.
329-336
- Xavier Llorà, Josep Maria Garrell i Guiu:
Inducing Partially-Defined Instances with Evolutionary Algorithms.
337-344
- Mario Marchand, John Shawe-Taylor:
Learning with the Set Covering Machine.
345-352
- Zvika Marx, Ido Dagan, Joachim M. Buhmann:
Coupled Clustering: a Method for Detecting Structural Correspondence.
353-360
- Amy McGovern, Andrew G. Barto:
Automatic Discovery of Subgoals in Reinforcement Learning using Diverse Density.
361-368
- Prasanth B. Nair, Arindam Choudhury, Andy J. Keane:
Some Greedy Algorithms for Sparse Nonlinear Regression.
369-376
- Andrew Y. Ng, Michael I. Jordan:
Convergence rates of the Voting Gibbs classifier, with application to Bayesian feature selection.
377-384
- Ilia Nouretdinov, Thomas Melluish, Volodya Vovk:
Ridge Regression Confidence Machine.
385-392
- Athanassios Papagelis, Dimitrios Kalles:
Breeding Decision Trees Using Evolutionary Techniques.
393-400
- Dan Pelleg, Andrew W. Moore:
Mixtures of Rectangles: Interpretable Soft Clustering.
401-408
- Theodore J. Perkins, Andrew G. Barto:
Lyapunov-Constrained Action Sets for Reinforcement Learning.
409-416
- Doina Precup, Richard S. Sutton, Sanjoy Dasgupta:
Off-Policy Temporal Difference Learning with Function Approximation.
417-424
- Soumya Ray, David Page:
Multiple Instance Regression.
425-432
- Marko Robnik-Sikonja, Igor Kononenko:
Comprehensible Interpretation of Relief's Estimates.
433-440
- Nicholas Roy, Andrew McCallum:
Toward Optimal Active Learning through Sampling Estimation of Error Reduction.
441-448
- Antonin Rozsypal, Miroslav Kubat:
Using the Genetic Algorithm to Reduce the Size of a Nearest-Neighbor Classifier and to Select Relevant Attributes.
449-456
- Peter Sand, Andrew W. Moore:
Repairing Faulty Mixture Models using Density Estimation.
457-464
- Manish Sarkar, Tze-Yun Leong:
Application of Fuzzy Similarity-Based Fractal Dimensions to Characterize Medical Time Series.
465-472
- Makoto Sato, Shigenobu Kobayashi:
Average-Reward Reinforcement Learning for Variance Penalized Markov Decision Problems.
473-480
- Tobias Scheffer, Stefan Wrobel:
Incremental Maximization of Non-Instance-Averaging Utility Functions with Applications to Knowledge Discovery Problems.
481-488
- Mark Schwabacher, Pat Langley:
Discovering Communicable Scientific Knowledge from Spatio-Temporal Data.
489-496
- Paola Sebastiani, Marco Ramoni:
Clustering Continuous Time Series.
497-504
- Marc Sebban, Richard Nock, Stéphane Lallich:
Boosting Neighborhood-Based Classifiers.
505-512
- Yevgeny Seldin, Gill Bejerano, Naftali Tishby:
Unsupervised Sequence Segmentation by a Mixture of Switching Variable Memory Markov Sources.
513-520
- Gregory Shakhnarovich, Ran El-Yaniv, Yoram Baram:
Smoothed Bootstrap and Statistical Data Cloning for Classifier Evaluation.
521-528
- Bryan Singer, Manuela M. Veloso:
Learning to Generate Fast Signal Processing Implementations.
529-536
- Peter Stone, Richard S. Sutton:
Scaling Reinforcement Learning toward RoboCup Soccer.
537-544
- Malcolm J. A. Strens, Andrew W. Moore:
Direct Policy Search using Paired Statistical Tests.
545-552
- Nigel Tao, Jonathan Baxter, Lex Weaver:
A Multi-Agent Policy-Gradient Approach to Network Routing.
553-560
- Franck Thollard:
Improving Probabilistic Grammatical Inference Core Algorithms with Post-processing Techniques.
561-568
- Anand Venkataraman:
A procedure for unsupervised lexicon learning.
569-576
- Kiri Wagstaff, Claire Cardie, Seth Rogers, Stefan Schrödl:
Constrained K-means Clustering with Background Knowledge.
577-584
- Marco Wiering:
Reinforcement Learning in Dynamic Environments using Instantiated Information.
585-592
- Jeremy L. Wyatt:
Exploration Control in Reinforcement Learning using Optimistic Model Selection.
593-600
- Eric P. Xing, Michael I. Jordan, Richard M. Karp:
Feature selection for high-dimensional genomic microarray data.
601-608
- Bianca Zadrozny, Charles Elkan:
Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers.
609-616
- Huajie Zhang, Charles X. Ling:
Learnability of Augmented Naive Bayes in Nonimal Domains.
617-623
- Tong Zhang:
Some Sparse Approximation Bounds for Regression Problems.
624-631
- Martin Zinkevich, Tucker R. Balch:
Symmetry in Markov Decision Processes and its Implications for Single Agent and Multiagent Learning.
632-
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