ICML 1998

66 papers

A Case Study in the Use of Theory Revision in Requirements Validation Thomas Leo McCluskey, Margaret Mary West

A Fast, Bottom-up Decision Tree Pruning Algorithm with Near-Optimal Generalization Michael J. Kearns, Yishay Mansour

A Learning Rate Analysis of Reinforcement Learning Algorithms in Finite-Horizon Frédérick Garçia, Seydina M. Ndiaye

A Neural Network Model for Prognostic Prediction W. Nick Street

A Process-Oriented Heuristic for Model Selection Pedro M. Domingos

A Randomized ANOVA Procedure for Comparing Performance Curves Justus H. Piater, Paul R. Cohen, Xiaoqin Zhang, Michael Atighetchi

A Supra-Classifier Architecture for Scalable Knowledge Reuse Kurt D. Bollacker, Joydeep Ghosh

An Analysis of Actor/Critic Algorithms Using Eligibility Traces: Reinforcement Learning with Imperfect Value Function Hajime Kimura, Shigenobu Kobayashi

An Analysis of Direct Reinforcement Learning in Non-Markovian Domains Mark D. Pendrith, Michael McGarity

An Efficient Boosting Algorithm for Combining Preferences Yoav Freund, Raj D. Iyer, Robert E. Schapire, Yoram Singer

An Experimental Evaluation of Coevolutive Concept Learning Cosimo Anglano, Attilio Giordana, Giuseppe Lo Bello, Lorenza Saitta

An Information-Theoretic Definition of Similarity Dekang Lin

An Investigation of Transformation-Based Learning in Discourse Ken Samuel, Sandra Carberry, K. Vijay-Shanker

Automatic Segmentation of Continuous Trajectories with Invariance to Nonlinear Warpings of Time Lawrence K. Saul

Bayesian Classifiers Are Large Margin Hyperplanes in a Hilbert Space Nello Cristianini, John Shawe-Taylor, Peter Sykacek

Bayesian Network Classification with Continuous Attributes: Getting the Best of Both Discretization and Parametric Fitting Nir Friedman, Moisés Goldszmidt, Thomas J. Lee

Classification Using Phi-Machines and Constructive Function Approximation Doina Precup, Paul E. Utgoff

Coevolutionary Learning: A Case Study Hugues Juillé, Jordan B. Pollack

Collaborative Filtering Using Weighted Majority Prediction Algorithms Atsuyoshi Nakamura, Naoki Abe

Combining Nearest Neighbor Classifiers Through Multiple Feature Subsets Stephen D. Bay

Employing EM and Pool-Based Active Learning for Text Classification Andrew Kachites McCallum, Kamal Nigam

Evolving Structured Programs with Hierarchical Instructions and Skip Nodes Rafal Salustowicz, Jürgen Schmidhuber

Feature Selection via Concave Minimization and Support Vector Machines Paul S. Bradley, Olvi L. Mangasarian

Finite-Time Regret Bounds for the Multiarmed Bandit Problem Nicolò Cesa-Bianchi, Paul Fischer

Generating Accurate Rule Sets Without Global Optimization Eibe Frank, Ian H. Witten

Genetic Programming and Deductive-Inductive Learning: A Multi-Strategy Approach Ricardo Aler, Daniel Borrajo, Pedro Isasi

Heading in the Right Direction Hagit Shatkay, Leslie Pack Kaelbling

Improving Text Classification by Shrinkage in a Hierarchy of Classes Andrew McCallum, Ronald Rosenfeld, Tom M. Mitchell, Andrew Y. Ng

Intra-Option Learning About Temporally Abstract Actions Richard S. Sutton, Doina Precup, Satinder Singh

KnightCap: A Chess Programm That Learns by Combining TD(lambda) with Game-Tree Search Jonathan Baxter, Andrew Tridgell, Lex Weaver

Learning a Language-Independent Representation for Terms from a Partially Aligned Corpus Michael L. Littman, Fan Jiang, Greg A. Keim

Learning Collaborative Information Filters Daniel Billsus, Michael J. Pazzani

Learning First-Order Acyclic Horn Programs from Entailment Chandra Reddy, Prasad Tadepalli

Learning Sorting and Decision Trees with POMDPs Blai Bonet, Hector Geffner

Learning the Grammar of Dance Joshua M. Stuart, Elizabeth Bradley

Learning to Drive a Bicycle Using Reinforcement Learning and Shaping Jette Randløv, Preben Alstrøm

Learning to Locate an Object in 3D Space from a Sequence of Camera Images Dimitris Margaritis, Sebastian Thrun

Local Cascade Generalization João Gama

Multi-Criteria Reinforcement Learning Zoltán Gábor, Zsolt Kalmár, Csaba Szepesvári

Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm Junling Hu, Michael P. Wellman

Multiple-Instance Learning for Natural Scene Classification Oded Maron, Aparna Lakshmi Ratan

Multistrategy Learning for Information Extraction Dayne Freitag

Near-Optimal Reinforcement Learning in Polynominal Time Michael J. Kearns, Satinder Singh

On Feature Selection: Learning with Exponentially Many Irrelevant Features as Training Examples Andrew Y. Ng

On the Power of Decision Lists Richard Nock, Pascal Jappy

Q2: Memory-Based Active Learning for Optimizing Noisy Continuous Functions Andrew W. Moore, Jeff G. Schneider, Justin A. Boyan, Mary S. Lee

Query Learning Strategies Using Boosting and Bagging Naoki Abe, Hiroshi Mamitsuka

Refining Initial Points for K-Means Clustering Paul S. Bradley, Usama M. Fayyad

Relational Reinforcement Learning Saso Dzeroski, Luc De Raedt, Hendrik Blockeel

Ridge Regression Learning Algorithm in Dual Variables Craig Saunders, Alexander Gammerman, Volodya Vovk

RL-TOPS: An Architecture for Modularity and Re-Use in Reinforcement Learning Malcolm R. K. Ryan, Mark D. Pendrith

Solving a Huge Number of Similar Tasks: A Combination of Multi-Task Learning and a Hierarchical Bayesian Approach Tom Heskes

Stochastic Resonance with Adaptive Fuzzy Systems Sanya Mitaim, Bart Kosko

Structural Machine Learning with Galois Lattice and Graphs Michel Liquiere, Jean Sallantin

Teaching an Agent to Test Students Gheorghe Tecuci, Harry Keeling

The Case Against Accuracy Estimation for Comparing Induction Algorithms Foster J. Provost, Tom Fawcett, Ron Kohavi

The Kernel-Adatron Algorithm: A Fast and Simple Learning Procedure for Support Vector Machines Thilo-Thomas Frieß, Nello Cristianini, Colin Campbell

The MAXQ Method for Hierarchical Reinforcement Learning Thomas G. Dietterich

The Problem with Noise and Small Disjuncts Gary M. Weiss, Haym Hirsh

Theory Refinement of Bayesian Networks with Hidden Variables Sowmya Ramachandran, Raymond J. Mooney

Top-Down Induction of Clustering Trees Hendrik Blockeel, Luc De Raedt, Jan Ramon

Using a Permutation Test for Attribute Selection in Decision Trees Eibe Frank, Ian H. Witten

Using Eligibility Traces to Find the Best Memoryless Policy in Partially Observable Markov Decision Processes John Loch, Satinder Singh

Using Learning for Approximation in Stochastic Processes Daphne Koller, Raya Fratkina

Value Function Based Production Scheduling Jeff G. Schneider, Justin A. Boyan, Andrew W. Moore

Well-Behaved Borgs, Bolos, and Berserkers Diana F. Gordon