ICML 1999

54 papers

A Hybrid Lazy-Eager Approach to Reducing the Computation and Memory Requirements of Local Parametric Learning Algorithms Yuanhui Zhou, Carla E. Brodley

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

Abstracting from Robot Sensor Data Using Hidden Markov Models Laura Firoiu, Paul R. Cohen

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

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

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

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

Approximation via Value Unification Paul E. Utgoff, David J. Stracuzzi

Associative Reinforcement Learning Using Linear Probabilistic Concepts Naoki Abe, Philip M. Long

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

Boosting a Strong Learner: Evidence Against the Minimum Margin Michael Bonnell Harries

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

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

Correcting Noisy Data Choh Man Teng

Detecting Motifs from Sequences Yuh-Jyh Hu, Suzanne B. Sandmeyer, Dennis F. Kibler

Discriminant Trees João Gama

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

Distributed Value Functions Jeff G. Schneider, Weng-Keen Wong, Andrew W. Moore, Martin A. Riedmiller

Efficient Non-Linear Control by Combining Q-Learning with Local Linear Controllers Hajime Kimura, Shigenobu Kobayashi

Expected Error Analysis for Model Selection Tobias Scheffer, Thorsten Joachims

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

Feature Engineering for Text Classification Sam Scott, Stan Matwin

Feature Selection as a Preprocessing Step for Hierarchical Clustering Luis Talavera

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

GA-Based Learning of Context-Free Grammars Using Tabular Representations Yasubumi Sakakibara, Mitsuhiro Kondo

Hierarchical Models for Screening of Iron Deficiency Anemia Igor V. Cadez, Christine E. McLaren, Padhraic Smyth, Geoffrey J. McLachlan

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

Implicit Imitation in Multiagent Reinforcement Learning Bob Price, Craig Boutilier

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

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

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

Learning Comprehensible Descriptions of Multivariate Time Series Mohammed Waleed Kadous

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

Learning Hierarchical Performance Knowledge by Observation Michael van Lent, John E. Laird

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

Learning to Optimally Schedule Internet Banner Advertisements Naoki Abe, Atsuyoshi Nakamura

Learning to Ride a Bicycle Using Iterated Phantom Induction Mark Brodie, Gerald DeJong

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

Least-Squares Temporal Difference Learning Justin A. Boyan

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

Machine-Learning Applications of Algorithmic Randomness Volodya Vovk, Alexander Gammerman, Craig Saunders

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

Model Selection in Unsupervised Learning with Applications to Document Clustering Shivakumar Vaithyanathan, Byron Dom

Monte Carlo Hidden Markov Models: Learning Non-Parametric Models of Partially Observable Stochastic Processes Sebastian Thrun, John Langford, Dieter Fox

Noise-Tolerant Recursive Best-First Induction Uros Pompe

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

OPT-KD: An Algorithm for Optimizing Kd-Trees Douglas A. Talbert, Douglas H. Fisher

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

Simple DFA Are Polynomially Probably Exactly Learnable from Simple Examples Rajesh Parekh, Vasant G. Honavar

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

The Alternating Decision Tree Learning Algorithm Yoav Freund, Llew Mason

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

Transductive Inference for Text Classification Using Support Vector Machines Thorsten Joachims

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