ICML-99 Accepted Papers

• Associative Reinforcement Learning using Linear Probabilistic Concepts
  Naoki Abe and Philip M. Long

• Learning to Optimally Schedule Internet Banner Advertisements
  Naoki Abe and Atsuyoshi Nakamura

• A Minimum Risk Metric for Nearest Neighbor Classification
  Enrico Blanzieri and Francesco Ricci

• Local Learning for Iterated Time-Series Prediction
  Gianluca Bontempi, Mauro Birattari, and Hugues Bersini

• Instance-Family Abstraction in Memory-Based Language Learning
  Antal van den Bosch

• Least-Squares Temporal Difference Learning
  Justin A. Boyan

• Learning to Ride a Bicycle using Iterated Phantom Induction
  Mark Brodie and Gerald DeJong

• Sonar-Based Mapping of Large-Scale Mobile Robot Environments using EM
  Wolfram Burgard, Dieter Fox, Hauke Jans, Christian Matenar, and Sebastian Thrun

• Hierarchical Models for Screening of Iron Deficiency Anemia
  I. V. Cadez, C. E. McLaren, P. Smyth, and G. J. McLachlan

• Combining Error-Driven Pruning and Classification for Partial Parsing
  Claire Cardie, Scott Mardis, and David Pierce

• AdaCost: Misclassification Cost-Sensitive Boosting
  Wei Fan, Salvatore J. Stolfo, Junxin Zhang, and Philip K. Chan

• Abstracting from Robot Sensor Data using Hidden Markov Models
  Laura Firoiu and Paul Cohen

• Making Better Use of Global Discretization
  Eibe Frank and Ian H. Witten

• The Alternating Decision Tree Learning Algorithm
  Yoav Freund and Llew Mason

• Discriminant Trees
  Joao Gama

• Experiments with Noise Filtering in a Medical Domain
  Dragan Gamberger, Nada Lavrac, and Ciril Groselj

• Learning User Evaluation Functions for Adaptive Scheduling Assistance
  Melinda T. Gervasio, Wayne Iba, and Pat Langley

• On Some Misbehaviour of Back-Propagation with Non-Normalized RBFNs and a Solution
  Attilio Giordana and Roberto Piola

• Boosting a Strong Learner
  Michael Harries

• Detecting Motifs from Sequences
  Yuh-Jyh Hu, Dennis Kibler, and Suzanne Sandmeyer

• Distributed Robotic Learning : Adaptive Behavior Acquisition for Distributed Autonomous Swimming Robot in Real World
  Daisuke Iijima, Wenwei Yu, Hiroshi Yokoi, and Yukinori Kakazu

• Transductive Inference for Text Classification using Support Vector Machines
  Thorsten Joachims

• Efficient Non-Linear Control by Combining Q-learning with Local Linear Controllers
  Hajime Kimura and Shigenobu Kobayashi

• Tractable Average-Case Analysis of Naive Bayesian Classifiers
  Pat Langley and Stephanie Sage

• Learning Hierarchical Performance Knowledge by Observation
  Michael van Lent and John Laird

• Correcting Noisy Data
  Choh Man Teng

• An Accelerated Chow and Liu Algorithm: Fitting Tree Distributions to High-Dimensional Sparse Data
  Marina Meila

• Feature Selection for Unbalanced Class Distribution and Naive Bayes
  Dunja Mladenic and Marko Grobelnik

• Combining Statistical Learning with a Knowledge-Based Approach -- A Case Study in Intensive Care Monitoring
  Katharina Morik, Peter Brockhausen, and Thorsten Joachims

• Policy Invariance Under Reward Transformations: Theory and Application to Reward Shaping
  Andrew Y. Ng, Daishi Harada, and Stuart Russell

• Learning Discriminatory and Descriptive Rules by an Inductive Logic Programming System
  Maziar Palhang and Arcot Sowmya

• Simple DFA are Polynomially Probably Exactly Learnable from Simple Examples
  Rajesh Parekh and Vasant Honavar

• Learning Policies with External Memory
  Leonid Peshkin, Nicolas Meuleau, and Leslie Pack Kaelbling

• Noise-Tolerant Recursive Best-First Induction
  Uros Pompe

• Implicit Imitation in Multiagent Reinforcement Learning
  Bob Price and Craig Boutilier

• Using Reinforcement Learning to Spider the Web Efficiently
  Jason Rennie and Andrew Kachites McCallum

• Attribute Dependencies, Understandability and Split Selection in Tree Based Models
  Marko Robnik-Sikonja and Igor Kononenko

• GA-based Learning of Context-Free Grammars using Tabular Representations
  Yasubumi Sakakibara and Mitsuhiro Kondo

• Expected Error Analysis for Model Selection
  Tobias Scheffer and Thorsten Joachims

• Distributed Value Functions
  Jeff Schneider, Weng-Keen Wong, Andrew Moore, and Martin Riedmiller

• Feature Engineering for Text Classification
  Sam Scott and Stan Matwin

• Feature Selection as a Preprocessing Step for Hierarchical Clustering
  Luis Talavera

• Learning Individual Search Indices for Diet Management
  Doug Talbert and Doug Fisher

• Active Learning for Natural Language Parsing and Information Extraction
  Cynthia A. Thompson, Mary Elaine Califf, and Raymond J. Mooney

• Sample-Based Hidden Markov Models
  Sebastian Thrun, John C. Langford, and Dieter Fox

• Approximation Via Value Unification
  Paul E.Utgoff and David J. Stracuzzi

• Model Selection in Unsupervised Learning with Applications To Document Clustering
  Shivakumar Vaithyanathan and Byron Dom

• Machine-Learning Applications of Algorithmic Randomness
  Volodya Vovk, Alex Gammerman, and Craig Saunders

• Learning Comprehensible Descriptions of Multivariate Time Series
  Mohammed Waleed Kadous

• Hierarchical Optimization of Policy-Coupled Semi-Markov Decision Processes
  Gang Wang and Sridhar Mahadevan

• Large Margin Decision Trees for Induction and Transduction
  Donghui Wu, Kristin P. Bennett, Nello Cristianini, and John Shawe-Taylor

• An Region-Based Learning Approach to Discovering Temporal Structures in Data
  Wei Zhang

• Lazy Bayesian Rules: A Lazy Semi-Naive Bayesian Learning Technique Competitive to Boosting Decision Trees
  Zijian Zheng, Geoffrey I. Webb, and Kai Ming Ting

• A Hybrid Lazy-Eager Approach to Reducing the Computation and Memory Requirements of Local Parametric Learning Algorithms
  Y. Zhou and C. Brodley