15. ICML 1998:
Madison,
Wisconsin,
USA
Jude W. Shavlik (Ed.):
Proceedings of the Fifteenth International Conference on Machine Learning (ICML 1998), Madison, Wisconsin, USA, July 24-27, 1998.
Morgan Kaufmann 1998, ISBN 1-55860-556-8 
- Naoki Abe, Hiroshi Mamitsuka:
Query Learning Strategies Using Boosting and Bagging.
1-9
- Ricardo Aler, Daniel Borrajo, Pedro Isasi:
Genetic Programming and Deductive-Inductive Learning: A Multi-Strategy Approach.
10-18
- Cosimo Anglano, Attilio Giordana, Giuseppe Lo Bello, Lorenza Saitta:
An Experimental Evaluation of Coevolutive Concept Learning.
19-27
- Jonathan Baxter, Andrew Tridgell, Lex Weaver:
KnightCap: A Chess Programm That Learns by Combining TD(lambda) with Game-Tree Search.
28-36
- Stephen D. Bay:
Combining Nearest Neighbor Classifiers Through Multiple Feature Subsets.
37-45
- Daniel Billsus, Michael J. Pazzani:
Learning Collaborative Information Filters.
46-54
- Hendrik Blockeel, Luc De Raedt, Jan Ramon:
Top-Down Induction of Clustering Trees.
55-63
- Kurt D. Bollacker, Joydeep Ghosh:
A Supra-Classifier Architecture for Scalable Knowledge Reuse.
64-72
- Blai Bonet, Hector Geffner:
Learning Sorting and Decision Trees with POMDPs.
73-81
- Paul S. Bradley, Olvi L. Mangasarian:
Feature Selection via Concave Minimization and Support Vector Machines.
82-90
- Paul S. Bradley, Usama M. Fayyad:
Refining Initial Points for K-Means Clustering.
91-99
- Nicolò Cesa-Bianchi, Paul Fischer:
Finite-Time Regret Bounds for the Multiarmed Bandit Problem.
100-108
- Nello Cristianini, John Shawe-Taylor, Peter Sykacek:
Bayesian Classifiers Are Large Margin Hyperplanes in a Hilbert Space.
109-117
- Thomas G. Dietterich:
The MAXQ Method for Hierarchical Reinforcement Learning.
118-126
- Pedro Domingos:
A Process-Oriented Heuristic for Model Selection.
127-135
- Saso Dzeroski, Luc De Raedt, Hendrik Blockeel:
Relational Reinforcement Learning.
136-143
- Eibe Frank, Ian H. Witten:
Generating Accurate Rule Sets Without Global Optimization.
144-151
- Eibe Frank, Ian H. Witten:
Using a Permutation Test for Attribute Selection in Decision Trees.
152-160
- Dayne Freitag:
Multistrategy Learning for Information Extraction.
161-169
- Yoav Freund, Raj D. Iyer, Robert E. Schapire, Yoram Singer:
An Efficient Boosting Algorithm for Combining Preferences.
170-178
- Nir Friedman, Moisés Goldszmidt, Thomas J. Lee:
Bayesian Network Classification with Continuous Attributes: Getting the Best of Both Discretization and Parametric Fitting.
179-187
- Thilo-Thomas Frieß, Nello Cristianini, Colin Campbell:
The Kernel-Adatron Algorithm: A Fast and Simple Learning Procedure for Support Vector Machines.
188-196
- Zoltán Gábor, Zsolt Kalmár, Csaba Szepesvári:
Multi-criteria Reinforcement Learning.
197-205
- Joao Gama:
Local Cascade Generalization.
206-214
- Frédérick Garcia, Seydina M. Ndiaye:
A Learning Rate Analysis of Reinforcement Learning Algorithms in Finite-Horizon.
215-223
- Diana F. Gordon:
Well-Behaved Borgs, Bolos, and Berserkers.
224-232
- Tom Heskes:
Solving a Huge Number of Similar Tasks: A Combination of Multi-Task Learning and a Hierarchical Bayesian Approach.
233-241
- Junling Hu, Michael P. Wellman:
Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm.
242-250
- Hugues Juillé, Jordan B. Pollack:
Coevolutionary Learning: A Case Study.
251-259
- Michael J. Kearns, Satinder P. Singh:
Near-Optimal Reinforcement Learning in Polynominal Time.
260-268
- Michael J. Kearns, Yishay Mansour:
A Fast, Bottom-Up Decision Tree Pruning Algorithm with Near-Optimal Generalization.
269-277
- Hajime Kimura, Shigenobu Kobayashi:
An Analysis of Actor/Critic Algorithms Using Eligibility Traces: Reinforcement Learning with Imperfect Value Function.
278-286
- Daphne Koller, Raya Fratkina:
Using Learning for Approximation in Stochastic Processes.
287-295
- Dekang Lin:
An Information-Theoretic Definition of Similarity.
296-304
- Michel Liquiere, Jean Sallantin:
Structural Machine Learning with Galois Lattice and Graphs.
305-313
- Michael L. Littman, Fan Jiang, Greg A. Keim:
Learning a Language-Independent Representation for Terms from a Partially Aligned Corpus.
314-322
- John Loch, Satinder P. Singh:
Using Eligibility Traces to Find the Best Memoryless Policy in Partially Observable Markov Decision Processes.
323-331
- Dimitris Margaritis, Sebastian Thrun:
Learning to Locate an Object in 3D Space from a Sequence of Camera Images.
332-340
- Oded Maron, Aparna Lakshmi Ratan:
Multiple-Instance Learning for Natural Scene Classification.
341-349
- Andrew McCallum, Kamal Nigam:
Employing EM and Pool-Based Active Learning for Text Classification.
350-358
- Andrew McCallum, Ronald Rosenfeld, Tom M. Mitchell, Andrew Y. Ng:
Improving Text Classification by Shrinkage in a Hierarchy of Classes.
359-367
- T. L. McCluskey, Margaret Mary West:
A Case Study in the Use of Theory Revision in Requirements Validation.
368-376
- Sanya Mitaim, Bart Kosko:
Stochastic Resonance with Adaptive Fuzzy Systems.
377-385
- Andrew W. Moore, Jeff G. Schneider, Justin A. Boyan, Mary S. Lee:
Q2: Memory-Based Active Learning for Optimizing Noisy Continuous Functions.
386-394
- Atsuyoshi Nakamura, Naoki Abe:
Collaborative Filtering Using Weighted Majority Prediction Algorithms.
395-403
- Andrew Y. Ng:
On Feature Selection: Learning with Exponentially Many Irrelevant Features as Training Examples.
404-412
- Richard Nock, Pascal Jappy:
On the Power of Decision Lists.
413-420
- Mark D. Pendrith, Michael McGarity:
An Analysis of Direct Reinforcement Learning in Non-Markovian Domains.
421-429
- Justus H. Piater, Paul R. Cohen, Xiaoqin Zhang, Michael Atighetchi:
A Randomized ANOVA Procedure for Comparing Performance Curves.
430-438
- Doina Precup, Paul E. Utgoff:
Classification Using Phi-Machines and Constructive Function Approximation.
439-444
- Foster J. Provost, Tom Fawcett, Ron Kohavi:
The Case against Accuracy Estimation for Comparing Induction Algorithms.
445-453
- Sowmya Ramachandran, Raymond J. Mooney:
Theory Refinement of Bayesian Networks with Hidden Variables.
454-462
- Jette Randløv, Preben Alstrøm:
Learning to Drive a Bicycle Using Reinforcement Learning and Shaping.
463-471
- Chandra Reddy, Prasad Tadepalli:
Learning First-Order Acyclic Horn Programs from Entailment.
472-480
- Malcolm R. K. Ryan, Mark D. Pendrith:
RL-TOPS: An Architecture for Modularity and Re-Use in Reinforcement Learning.
481-487
- Rafal Salustowicz, Jürgen Schmidhuber:
Evolving Structured Programs with Hierarchical Instructions and Skip Nodes.
488-496
- Ken Samuel, Sandra Carberry, K. Vijay-Shanker:
An Investigation of Transformation-Based Learning in Discourse.
497-505
- Lawrence K. Saul:
Automatic Segmentation of Continuous Trajectories with Invariance to Nonlinear Warpings of Time.
506-514
- Craig Saunders, Alexander Gammerman, Volodya Vovk:
Ridge Regression Learning Algorithm in Dual Variables.
515-521
- Jeff G. Schneider, Justin A. Boyan, Andrew W. Moore:
Value Function Based Production Scheduling.
522-530
- Hagit Shatkay, Leslie Pack Kaelbling:
Heading in the Right Direction.
531-539
- W. Nick Street:
A Neural Network Model for Prognostic Prediction.
540-546
- Joshua M. Stuart, Elizabeth Bradley:
Learning the Grammar of Dance.
547-555
- Richard S. Sutton, Doina Precup, Satinder P. Singh:
Intra-Option Learning about Temporally Abstract Actions.
556-564
- Gheorghe Tecuci, Harry Keeling:
Teaching an Agent to Test Students.
565-573
- Gary M. Weiss, Haym Hirsh:
The Problem with Noise and Small Disjuncts.
574-
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