19. ICML 2002: Sydney, NSW, Australia
Claude Sammut, Achim G. Hoffmann (Eds.):
Machine Learning, Proceedings of the Nineteenth International Conference (ICML 2002), University of New South Wales, Sydney, Australia, July 8-12, 2002.
Morgan Kaufmann 2002, ISBN 1-55860-873-7 
- Douglas Aberdeen, Jonathan Baxter:
Scalable Internal-State Policy-Gradient Methods for POMDPs.
3-10
- Érick Alphonse, Stan Matwin:
Feature Subset Selection and Inductive Logic Programming.
11-18
- Valentina Bayer Zubek, Thomas G. Dietterich:
Pruning Improves Heuristic Search for Cost-Sensitive Learning.
19-26
- Sugato Basu, Arindam Banerjee, Raymond J. Mooney:
Semi-supervised Clustering by Seeding.
27-34
- Jacques Ales Bianchetti, Céline Rouveirol, Michèle Sebag:
Constraint-based Learning of Long Relational Concepts.
35-42
- Joseph Bockhorst, Mark Craven:
Exploiting Relations Among Concepts to Acquire Weakly Labeled Training Data.
43-50
- Blai Bonet:
An epsilon-Optimal Grid-Based Algorithm for Partially Observable Markov Decision Processes.
51-58
- Björn Bringmann, Stefan Kramer, Friedrich Neubarth, Hannes Pirker, Gerhard Widmer:
Transformation-Based Regression.
59-66
- Zheng Chen, Liu Wenyin, Feng Zhang:
A New Statistical Approach to Personal Name Extraction.
67-74
- Michael Chisholm, Prasad Tadepalli:
Learning Decision Rules by Randomized Iterative Local Search.
75-82
- Elisabeth Crawford, Judy Kay, Eric McCreath:
IEMS - The Intelligent Email Sorter.
83-90
- Denver Dash, Gregory F. Cooper:
Exact model averaging with naive Bayesian classifiers.
91-98
- Dennis DeCoste:
Anytime Interval-Valued Outputs for Kernel Machines: Fast Support Vector Machine Classification via Distance Geometry.
99-106
- Thomas G. Dietterich, Dídac Busquets, Ramon López de Mántaras, Carles Sierra:
Action Refinement in Reinforcement Learning by Probability Smoothing.
107-114
- Kurt Driessens, Saso Dzeroski:
Integrating Experimentation and Guidance in Relational Reinforcement Learning.
115-122
- Saso Dzeroski, Bernard Zenko:
Is Combining Classifiers Better than Selecting the Best One.
123-130
- Tapio Elomaa, Juho Rousu:
Fast Minimum Training Error Discretization.
131-138
- César Ferri, Peter A. Flach, José Hernández-Orallo:
Learning Decision Trees Using the Area Under the ROC Curve.
139-146
- Leigh J. Fitzgibbon, David L. Dowe, Lloyd Allison:
Univariate Polynomial Inference by Monte Carlo Message Length Approximation.
147-154
- Joao Gama:
An Analysis of Functional Trees.
155-162
- Dragan Gamberger, Nada Lavrac:
Descriptive Induction through Subgroup Discovery: A Case Study in a Medical Domain.
163-170
- Ashutosh Garg, Sariel Har-Peled, Dan Roth:
On generalization bounds, projection profile, and margin distribution.
171-178
- Thomas Gärtner, Peter A. Flach, Adam Kowalczyk, Alex J. Smola:
Multi-Instance Kernels.
179-186
- Rayid Ghani:
Combining Labeled and Unlabeled Data for MultiClass Text Categorization.
187-194
- Mohammad Ghavamzadeh, Sridhar Mahadevan:
Hierarchically Optimal Average Reward Reinforcement Learning.
195-202
- Amir Globerson, Naftali Tishby:
Sufficient Dimensionality Reduction - A novel Analysis Method.
203-210
- Michael Goebel, Patricia J. Riddle, Mike Barley:
A Unified Decomposition of Ensemble Loss for Predicting Ensemble Performance.
211-218
- Jesus A. Gonzalez, Lawrence B. Holder, Diane J. Cook:
Graph-Based Relational Concept Learning.
219-226
- Carlos Guestrin, Michail G. Lagoudakis, Ronald Parr:
Coordinated Reinforcement Learning.
227-234
- Carlos Guestrin, Relu Patrascu, Dale Schuurmans:
Algorithm-Directed Exploration for Model-Based Reinforcement Learning in Factored MDPs.
235-242
- Bernhard Hengst:
Discovering Hierarchy in Reinforcement Learning with HEXQ.
243-250
- Colin K. M. Ho:
Classification Value Grouping.
251-258
- David Jensen, Jennifer Neville:
Linkage and Autocorrelation Cause Feature Selection Bias in Relational Learning.
259-266
- Sham Kakade, John Langford:
Approximately Optimal Approximate Reinforcement Learning.
267-274
- Sham Kakade, Yee Whye Teh, Sam T. Roweis:
An Alternate Objective Function for Markovian Fields.
275-282
- Sepandar D. Kamvar, Dan Klein, Christopher D. Manning:
Interpreting and Extending Classical Agglomerative Clustering Algorithms using a Model-Based approach.
283-290
- Hisashi Kashima, Teruo Koyanagi:
Kernels for Semi-Structured Data.
291-298
- S. Sathiya Keerthi, Kaibo Duan, Shirish Krishnaj Shevade, Aun Neow Poo:
A Fast Dual Algorithm for Kernel Logistic Regression.
299-306
- Dan Klein, Sepandar D. Kamvar, Christopher D. Manning:
From Instance-level Constraints to Space-Level Constraints: Making the Most of Prior Knowledge in Data Clustering.
307-314
- Risi Imre Kondor, John D. Lafferty:
Diffusion Kernels on Graphs and Other Discrete Input Spaces.
315-322
- Gert R. G. Lanckriet, Nello Cristianini, Peter L. Bartlett, Laurent El Ghaoui, Michael I. Jordan:
Learning the Kernel Matrix with Semi-Definite Programming.
323-330
- John Langford:
Combining Trainig Set and Test Set Bounds.
331-338
- John Langford, Martin Zinkevich, Sham Kakade:
Competitive Analysis of the Explore/Exploit Tradeoff.
339-346
- Pat Langley, Javier Nicolás Sánchez, Ljupco Todorovski, Saso Dzeroski:
Inducing Process Models from Continuous Data.
347-354
- Adam Laud, Gerald DeJong:
Reinforcement Learning and Shaping: Encouraging Intended Behaviors.
355-362
- Guy Lebanon, John D. Lafferty:
Cranking: Combining Rankings Using Conditional Probability Models on Permutations.
363-370
- Christopher Leckie, Kotagiri Ramamohanarao:
Learning to Share Distributed Probabilistic Beliefs.
371-378
- Yaoyong Li, Hugo Zaragoza, Ralf Herbrich, John Shawe-Taylor, Jaz S. Kandola:
The Perceptron Algorithm with Uneven Margins.
379-386
- Bing Liu, Wee Sun Lee, Philip S. Yu, Xiaoli Li:
Partially Supervised Classification of Text Documents.
387-394
- Huan Liu, Hiroshi Motoda, Lei Yu:
Feature Selection with Selective Sampling.
395-402
- Fletcher Lu, Relu Patrascu, Dale Schuurmans:
Investigating the Maximum Likelihood Alternative to TD(lambda).
403-410
- Artur Merke, Ralf Schoknecht:
A Necessary Condition of Convergence for Reinforcement Learning with Function Approximation.
411-418
- Carsten Meyer, Peter Beyerlein:
Towards "Large Margin" Speech Recognizers by Boosting and Discriminative Training.
419-426
- Dunja Mladenic:
Learning word normalization using word suffix and context from unlabeled data.
427-434
- Ion Muslea, Steven Minton, Craig A. Knoblock:
Active + Semi-supervised Learning = Robust Multi-View Learning.
435-442
- Ion Muslea, Steven Minton, Craig A. Knoblock:
Adaptive View Validation: A First Step Towards Automatic View Detection.
443-450
- Jangmin O, Jae Won Lee, Byoung-Tak Zhang:
Stock Trading System Using Reinforcement Learning with Cooperative Agents.
451-458
- Tim Oates, Devina Desai, Vinay Bhat:
Learning k-Reversible Context-Free Grammars from Positive Structural Examples.
459-465
- Nuria Oliver, Ashutosh Garg:
MMIHMM: Maximum Mutual Information Hidden Markov Models.
466-473
- Anand Panangadan, Michael G. Dyer:
Learning Spatial and Temporal Correlation for Navigation in a 2-Dimensional Continuous World.
474-481
- Seong-Bae Park, Byoung-Tak Zhang:
A Boosted Maximum Entropy Model for Learning Text Chunking.
482-489
- Theodore J. Perkins, Mark D. Pendrith:
On the Existence of Fixed Points for Q-Learning and Sarsa in Partially Observable Domains.
490-497
- Leonid Peshkin, Christian R. Shelton:
Learning from Scarce Experience.
498-505
- Marc Pickett, Andrew G. Barto:
PolicyBlocks: An Algorithm for Creating Useful Macro-Actions in Reinforcement Learning.
506-513
- Bhavani Raskutti, Herman L. Ferrá, Adam Kowalczyk:
Using Unlabelled Data for Text Classification through Addition of Cluster Parameters.
514-521
- Malcolm R. K. Ryan:
Using Abstract Models of Behaviours to Automatically Generate Reinforcement Learning Hierarchies.
522-529
- Craig Saunders, Hauke Tschach, John Shawe-Taylor:
Syllables and other String Kernel Extensions.
530-537
- Robert E. Schapire, Marie Rochery, Mazin G. Rahim, Narendra K. Gupta:
Incorporating Prior Knowledge into Boosting.
538-545
- Robert E. Schapire, Peter Stone, David A. McAllester, Michael L. Littman, János A. Csirik:
Modeling Auction Price Uncertainty Using Boosting-based Conditional Density Estimation.
546-553
- Alexander K. Seewald:
How to Make Stacking Better and Faster While Also Taking Care of an Unknown Weakness.
554-561
- Sandeep Seri, Prasad Tadepalli:
Model-based Hierarchical Average-reward Reinforcement Learning.
562-569
- Daniel G. Shapiro, Pat Langley:
Separating Skills from Preference: Using Learning to Program by Reward.
570-577
- Noam Slonim, Gill Bejerano, Shai Fine, Naftali Tishby:
Discriminative Feature Selection via Multiclass Variable Memory Markov Model.
578-585
- David Stirling:
Learning to Fly by Controlling Dynamic Instabilities.
586-593
- David J. Stracuzzi, Paul E. Utgoff:
Randomized Variable Elimination.
594-601
- Malcolm J. A. Strens, Mark Bernhardt, Nicholas Everett:
Markov Chain Monte Carlo Sampling using Direct Search Optimization.
602-609
- Dorian Suc, Ivan Bratko:
Qualitative reverse engineering.
610-617
- Fumio Takechi, Einoshin Suzuki:
Finding an Optimal Gain-Ratio Subset-Split Test for a Set-Valued Attribute in Decision Tree Induction.
618-625
- Loo-Nin Teow, Haifeng Liu, Hwee Tou Ng, Eric Yap:
Refining the Wrapper Approach - Smoothed Error Estimates for Feature Selection.
626-633
- Shien-Shin Tham, Arnaud Doucet, Kotagiri Ramamohanarao:
Sparse Bayesian Learning for Regression and Classification using Markov Chain Monte Carlo.
634-641
- Kai Ming Ting:
Issues in Classifier Evaluation using Optimal Cost Curves.
642-649
- Yong Wang, Ian H. Witten:
Modeling for Optimal Probability Prediction.
650-657
- Xindong Wu, Chengqi Zhang, Shichao Zhang:
Mining Both Positive and Negative Association Rules.
658-665
- Ying Yang, Geoffrey I. Webb:
Non-Disjoint Discretization for Naive-Bayes Classifiers.
666-673
- Huajie Zhang, Charles X. Ling:
Representational Upper Bounds of Bayesian Networks.
674-681
- Qi Zhang, Sally A. Goldman, Wei Yu, Jason E. Fritts:
Content-Based Image Retrieval Using Multiple-Instance Learning.
682-689
- Tong Zhang:
Statistical Behavior and Consistency of Support Vector Machines, Boosting, and Beyond.
690-700
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