16. ICML 1999:
Bled,
Slovenia
Ivan Bratko, Saso Dzeroski (Eds.):
Proceedings of the Sixteenth International Conference on Machine Learning (ICML 1999), Bled, Slovenia, June 27 - 30, 1999.
Morgan Kaufmann 1999, ISBN 1-55860-612-2 
- Naoki Abe, Philip M. Long:
Associative Reinforcement Learning using Linear Probabilistic Concepts.
3-11
- Naoki Abe, Atsuyoshi Nakamura:
Learning to Optimally Schedule Internet Banner Advertisements.
12-21
- Enrico Blanzieri, Francesco Ricci:
A Minimum Risk Metric for Nearest Neighbor Classification.
22-31
- Gianluca Bontempi, Mauro Birattari, Hugues Bersini:
Local Learning for Iterated Time-Series Prediction.
32-38
- Antal van den Bosch:
Instance-Family Abstraction in Memory-Based Language Learning.
39-48
- Justin A. Boyan:
Least-Squares Temporal Difference Learning.
49-56
- Mark Brodie, Gerald DeJong:
Learning to Ride a Bicycle using Iterated Phantom Induction.
57-66
- Wolfram Burgard, Dieter Fox, Hauke Jans, Christian Matenar, Sebastian Thrun:
Sonar-Based Mapping of Large-Scale Mobile Robot Environments using EM.
67-76
- Igor V. Cadez, Christine E. McLaren, Padhraic Smyth, Geoffrey J. McLachlan:
Hierarchical Models for Screening of Iron Deficiency Anemia.
77-86
- Claire Cardie, Scott Mardis, David R. Pierce:
Combining Error-Driven Pruning and Classification for Partial Parsing.
87-96
- Wei Fan, Salvatore J. Stolfo, Junxin Zhang, Philip K. Chan:
AdaCost: Misclassification Cost-Sensitive Boosting.
97-105
- Laura Firoiu, Paul R. Cohen:
Abstracting from Robot Sensor Data using Hidden Markov Models.
106-114
- Eibe Frank, Ian H. Witten:
Making Better Use of Global Discretization.
115-123
- Yoav Freund, Llew Mason:
The Alternating Decision Tree Learning Algorithm.
124-133
- Joao Gama:
Discriminant Trees.
134-142
- Dragan Gamberger, Nada Lavrac, Ciril Groselj:
Experiments with Noise Filtering in a Medical Domain.
143-151
- Melinda T. Gervasio, Wayne Iba, Pat Langley:
Learning User Evaluation Functions for Adaptive Scheduling Assistance.
152-161
- Attilio Giordana, Roberto Piola:
On Some Misbehaviour of Back-Propagation with Non-Normalized RBFNs and a Solution.
162-170
- Michael Bonnell Harries:
Boosting a Strong Learner: Evidence Against the Minimum Margin.
171-180
- Yuh-Jyh Hu, Suzanne B. Sandmeyer, Dennis F. Kibler:
Detecting Motifs from Sequences.
181-190
- Daisuke Iijima, Wenwei Yu, Hiroshi Yokoi, Yukinori Kakazu:
Distributed Robotic Learning: Adaptive Behavior Acquisition for Distributed Autonomous Swimming Robot in Real World.
191-199
- Thorsten Joachims:
Transductive Inference for Text Classification using Support Vector Machines.
200-209
- Hajime Kimura, Shigenobu Kobayashi:
Efficient Non-Linear Control by Combining Q-learning with Local Linear Controllers.
210-219
- Pat Langley, Stephanie Sage:
Tractable Average-Case Analysis of Naive Bayesian Classifiers.
220-228
- Michael van Lent, John E. Laird:
Learning Hierarchical Performance Knowledge by Observation.
229-238
- Choh-Man Teng:
Correcting Noisy Data.
239-248
- Marina Meila:
An Accelerated Chow and Liu Algorithm: Fitting Tree Distributions to High-Dimensional Sparse Data.
249-257
- Dunja Mladenic, Marko Grobelnik:
Feature Selection for Unbalanced Class Distribution and Naive Bayes.
258-267
- Katharina Morik, Peter Brockhausen, Thorsten Joachims:
Combining Statistical Learning with a Knowledge-Based Approach - A Case Study in Intensive Care Monitoring.
268-277
- Andrew Y. Ng, Daishi Harada, Stuart J. Russell:
Policy Invariance Under Reward Transformations: Theory and Application to Reward Shaping.
278-287
- Maziar Palhang, Arcot Sowmya:
Learning Discriminatory and Descriptive Rules by an Inductive Logic Programming System.
288-297
- Rajesh Parekh, Vasant Honavar:
Simple DFA are Polynomially Probably Exactly Learnable from Simple Examples.
298-306
- Leonid Peshkin, Nicolas Meuleau, Leslie Pack Kaelbling:
Learning Policies with External Memory.
307-314
- Uros Pompe:
Noise-Tolerant Recursive Best-First Induction.
315-324
- Bob Price, Craig Boutilier:
Implicit Imitation in Multiagent Reinforcement Learning.
325-334
- Jason Rennie, Andrew McCallum:
Using Reinforcement Learning to Spider the Web Efficiently.
335-343
- Marko Robnik-Sikonja, Igor Kononenko:
Attribute Dependencies, Understandability and Split Selection in Tree Based Models.
344-353
- Yasubumi Sakakibara, Mitsuhiro Kondo:
GA-based Learning of Context-Free Grammars using Tabular Representations.
354-360
- Tobias Scheffer, Thorsten Joachims:
Expected Error Analysis for Model Selection.
361-370
- Jeff G. Schneider, Weng-Keen Wong, Andrew W. Moore, Martin A. Riedmiller:
Distributed Value Functions.
371-378
- Sam Scott, Stan Matwin:
Feature Engineering for Text Classification.
379-388
- Luis Talavera:
Feature Selection as a Preprocessing Step for Hierarchical Clustering.
389-397
- Douglas A. Talbert, Douglas H. Fisher:
OPT-KD: An Algorithm for Optimizing Kd-Trees.
398-405
- Cynthia A. Thompson, Mary Elaine Califf, Raymond J. Mooney:
Active Learning for Natural Language Parsing and Information Extraction.
406-414
- Sebastian Thrun, John Langford, Dieter Fox:
Monte Carlo Hidden Markov Models: Learning Non-Parametric Models of Partially Observable Stochastic Processes.
415-424
- Paul E. Utgoff, David J. Stracuzzi:
Approximation Via Value Unification.
425-432
- Shivakumar Vaithyanathan, Byron Dom:
Model Selection in Unsupervised Learning with Applications To Document Clustering.
433-443
- Volodya Vovk, Alexander Gammerman, Craig Saunders:
Machine-Learning Applications of Algorithmic Randomness.
444-453
- Mohammed Waleed Kadous:
Learning Comprehensible Descriptions of Multivariate Time Series.
454-463
- Gang Wang, Sridhar Mahadevan:
Hierarchical Optimization of Policy-Coupled Semi-Markov Decision Processes.
464-473
- Donghui Wu, Kristin P. Bennett, Nello Cristianini, John Shawe-Taylor:
Large Margin Trees for Induction and Transduction.
474-483
- Wei Zhang:
An Region-Based Learning Approach to Discovering Temporal Structures in Data.
484-492
- Zijian Zheng, Geoffrey I. Webb, Kai Ming Ting:
Lazy Bayesian Rules: A Lazy Semi-Naive Bayesian Learning Technique Competitive to Boosting Decision Trees.
493-502
- Yuanhui Zhou, Carla E. Brodley:
A Hybrid Lazy-Eager Approach to Reducing the Computation and Memory Requirements of Local Parametric Learning Algorithms.
503-
Copyright © Tue Feb 9 19:29:58 2010
by Michael Ley (ley@uni-trier.de)
