ICML 1989

128 papers

"Learning by Instruction" in Connectionist Systems Joachim Diederich

A Bootstrapping Approach to Concept Clustering Katharina Morik, Jörg-Uwe Kietz

A Description of Preference Criterion in Constructive Learning: A Discussion of Basis Issues Jianping Zhang, Ryszard S. Michalski

A Formal Framework for Learning in Embedded Systems Leslie Pack Kaelbling

A Framework for Improving Efficiency and Accuracy James Wogulis

A Knowledge-Level Analysis of Informing Jane Yung-jen Hsu

A Mathematical Framework for Studying Representation Robert C. Holte, Robert M. Zimmer

A Retrieval Model Using Feature Selection Colleen M. Seifert

A Role for Anticipation in Reactive Systems That Learn Steven D. Whitehead, Dana H. Ballard

A Schema for an Integrated Learning System Michael Wollowski

A Theory of Justified Reformulations Devika Subramanian

A Tight Integration of Deductive Learning Gerhard Widmer

Adaptation-Based Explanation: Explanations as Cases Alex Kass

Adaptive Learning of Decision-Theoretic Search Control Knowledge Eric Wefald, Stuart J. Russell

An Empirical Analysis of EBL Approaches for Learning Plan Schemata Jude W. Shavlik

An Experimental Comparison of Human and Machine Learning Formalisms Stephen H. Muggleton, Michael Bain, Jean Hayes Michie, Donald Michie

An Exploration into Incremental Learning: The INFLUENCE System Antoine Cornuéjols

An Incremental Genetic Algorithm for Real-Time Learning Terence C. Fogarty

An Object-Oriented Representation for Search Algorithms Jack Mostow

An Ounce of Knowledge Is Worth a Ton of Data: Quantitative Studies of the Trade-Off Between Expertise and Data Based on Statistically Well-Founded Empirical Induction Brian R. Gaines

Approximating Learned Search Control Knowledge Melissa P. Chase, Monte Zweben, Richard L. Piazza, John D. Burger, Paul P. Maglio, Haym Hirsh

Atoms of Learning II: Adaptive Strategies a Study of Two-Person Zero-Sum Competition Oliver G. Selfridge

Augmenting Domain Theory for Explanation-Based Generalization Kamal M. Ali

Automatic Construction of a Hierarchical Generate-and-Test Algorithm Sunil Mohan, Chris Tong

Bacon, Data Analysis and Artificial Intelligence Cullen Schaffer

Building a Learning Bias from Perceived Dependencies Christian de Sainte Marie

Combining Case-Based Reasoning, Explanation-Based Learning, and Learning Form Instruction Michael Redmond

Combining Empirical and Analytical Learning with Version Spaces Haym Hirsh

Combining Explanation-Based Learning and Artificial Neural Networks Jude W. Shavlik, Geoffrey G. Towell

Comparing Systems and Analyzing Functions to Improve Constructive Induction Larry A. Rendell

Compiling Learning Vocabulary from a Performance System Description Richard M. Keller

Concept Discovery Through Utilization of Invariance Embedded in the Description Language Mieczyslaw M. Kokar

Conceptual Clustering of Explanations Jungsoon P. Yoo, Douglas H. Fisher

Conceptual Clustering of Mean-Ends Plans Hua Yang, Douglas H. Fisher

Constructive Induction by Analogy Luc De Raedt, Maurice Bruynooghe

Constructive Induction Framework Pankaj Mehra

Controlling Search for the Consequences of New Information During Knowledge Integration Kenneth S. Murray, Bruce W. Porter

Cost-Sensitive Concept Learning of Sensor Use in Approach Ad Recognition Ming Tan, Jeffrey C. Schlimmer

Declarative Bias for Structural Domains Benjamin N. Grosof, Stuart J. Russell

Deduction in Top-Down Inductive Learning Francesco Bergadano, Attilio Giordana, S. Ponsero

Discovering Admissible Search Heuristics by Abstracting and Optimizing Jack Mostow, Armand Prieditis

Discovering Mathematical Operation Definitions Michael H. Sims, John L. Bresina

Discovering Problem Solving Strategies: What Humans Do and Machines Don't (Yet) Kurt VanLehn

Empirical Substructure Discovery Lawrence B. Holder

Enriching Vocabularies by Generalizing Explanation Structures Richard Maclin, Jude W. Shavlik

Error Correction in Constructive Induction George Drastal, Regine Meunier, Stan Raatz

Evaluating Alternative Instance Representations Sharad Saxena

Evaluating Bias During Pac-Learning Lonnie Chrisman

Exemplar-Based Theory Rejection: An Approach to the Experience Consistency Problem Shankar A. Rajamoney

Experiments in Robot Learning Matthew T. Mason, Alan D. Christiansen, Tom M. Mitchell

Explanation Based Learning as Constrained Search David Haines

Explanation-Based Acceleration of Similarity-Based Learning Masayuki Numao, Masamichi Shimura

Explanation-Based Learning of Reactive Operations Melinda T. Gervasio, Gerald DeJong

Explanation-Based Learning with Week Domain Theories Michael J. Pazzani

Finding New Rules for Incomplete Theories: Explicit Biases for Induction with Contextual Information Andrea Pohoreckyj Danyluk

Focused Concept Formation John H. Gennari

Generalized Recursive Splitting Algorithms for Learning Hybrid Concepts Bruce L. Lambert, David K. Tcheng, Stephen C. Y. Lu

Higher-Order and Modal Logic as a Framework for Explanation-Based Generalization Scott Dietzen, Frank Pfenning

Identifying Knowledge Base Deficiencies by Observing User Behavior Keith R. Levi, Valerie L. Shalin, David L. Perschbacher

Imprecise Concept Learning Within a Growing Language Zbigniew W. Ras, Maria Zemankova

Improved Training via Incremental Learning Paul E. Utgoff

Improving Decision-Making on the Basis of Experience Bruce Krulwich, Gregg Collins, Lawrence Birnbaum

Improving Explanation-Based Indexing with Empirical Learning Ralph Barletta, Randy Kerber

Incremental Batch Learning Scott H. Clearwater, Tze-Pin Chen, Haym Hirsh, Bruce G. Buchanan

Incremental Clustering by Minimizing Representation Length Jakub Segen

Incremental Concept Formation with Composite Objects Kevin Thompson, Pat Langley

Incremental Learning of Control Strategies with Genetic Algorithms John J. Grefenstette

Incremental, Instance-Based Learning of Independent and Graded Concept Descriptions David W. Aha

Induction of Decision Trees from Inconclusive Data W. Scott Spangler, Usama M. Fayyad, Ramasamy Uthurusamy

Induction over the Unexplained: Integrated Learning of Concepts with Both Explainable and Conventional Aspects Raymond J. Mooney, Dirk Ourston

Inductive Learning with BCT Philip K. Chan

Information Filters and Their Implementation in the SYLLOG System Shaul Markovitch, Paul D. Scott

Integrating Learning in a Neural Network Bruce F. Katz

Issues in the Justification-Based Diagnosis of Planning Failures Lawrence Birnbaum, Gregg Collins, Bruce Krulwich

Knowledge Acquisition Planning: Results and Prospects Lawrence Hunter

Knowledge Base Refinement and Theory Revision Allen Ginsberg

Knowledge Base Refinement as Improving an Incorrect, Inconsistent and Incomplete Domain Theory David C. Wilkins, Kok-Wah Tan

Knowledge Intensive Induction Michel Manago

Knowledge-Based Feature Generation James P. Callan

Labor Saving New Distinctions John Woodfill

Learning Appropriate Abstractions for Planning in Formation Problems Nicholas S. Flann

Learning by Analyzing Fortuitous Occurrences Steve A. Chien

Learning Classification Rules Using Bayes Wray L. Buntine

Learning Decision Rules for Scheduling Problems: A Classifier Hybrid Approach Mike R. Hilliard, Gunar E. Liepins, Gita Rangarajan, Mark R. Palmer

Learning from Opportunity Timothy M. Converse, Kristian J. Hammond, Mitchell Marks

Learning from Plausible Explanations Tom Fawcett

Learning Hierarchies of Abstraction Spaces Craig A. Knoblock

Learning Invariants from Explanations Jean-Francois Puget

Learning Procedural Knowledge in the EBG Context Stan Matwin, Johanne Morin

Learning Tactical Plans for Pilot Aiding Keith R. Levi, David L. Perschbacher, Valerie L. Shalin

Learning the Behavior of Dynamical Systems Form Examples Jan Paredis

Learning to Plan in Complex Domains David Rudy, Dennis F. Kibler

Learning to Recognize Plans Involving Affect Paul O'Rorke, Timothy Cain, Andrew Ortony

Learning to Retrieve Useful Information for Problem Solving Randolph M. Jones

Limitations on Inductive Learning Thomas G. Dietterich

Multi-Strategy Learning in Nonhomongeneous Domain Theories Gheorghe Tecuci, Yves Kodratoff

New Empirical Learning Mechanisms Perform Significantly Better in Real Life Domains Matjaz Gams, Aram Karalic

On Becoming Reactive Jim Blythe, Tom M. Mitchell

One-Sided Algorithms for Integrating Empirical and Explanation-Based Learning Wendy Sarrett, Michael J. Pazzani

Overcoming Feature Space Bias in a Reactive Environment Hans Tallis

Participatory Learning: A Constructivist Model Ronald R. Yager, Kenneth M. Ford

Planning Approximate Plans for Use in the Real World Prasad Tadepalli

Processing Issues in Comparisons of Symbolic and Connectionist Learning Systems Douglas H. Fisher, Kathleen B. McKusick, Raymond J. Mooney, Jude W. Shavlik, Geoffrey G. Towell

Reducing Redundant Learning Joel D. Martin

Reducing Search and Learning Goal Preferences Steven Morris

Refining Representations to Improve Problem Solving Quality Jeffrey C. Schlimmer

Reformation from State Space to Reduction Space Patricia J. Riddle

Representational Issues in Machine Learning Devika Subramanian

Screening Hypotheses with Explicit Bias Diana F. Gordon

Signal Detection Theory: Valuable Tools for Evaluating Inductive Learning Kent A. Spackman

Some Results on the Complexity of Knowledge-Based Refinement Marco Valtorta

The Induction of Probabilistic Rule Sets - The Itrule Algorithm Rodney M. Goodman, Padhraic Smyth

The Role of Experimentation in Scientific Theory Revision Deepak Kulkarni, Herbert A. Simon

Theory Formation by Abduction: Initial Results of a Case Study Based on the Chemical Revolution Paul O'Rorke, Steven Morris, David Schulenburg

Toward Automated Rational Reconstruction: A Case Study Chris Tong, Phil Franklin

Towards a Formal Analysis of EBL Russell Greiner

Tower of Hanoi with Connectionist Networks: Learning New Features Charles W. Anderson

Two Algorithms That Learn DNF by Discovering Relevant Features Giulia Pagallo, David Haussler

Uncertainty Based Selection of Learning Experiences Paul D. Scott, Shaul Markovitch

Unifying Themes in Empirical and Explanation-Based Learning Pat Langley

Unknown Attribute Values in Induction J. Ross Quinlan

Using Concept Hierarchies to Organize Plan Knowledge John A. Allen, Pat Langley

Using Determinations in EBL: A Solution to the Incomplete Theory Problem Sridhar Mahadevan

Using Domain Knowledge to Aid Scientific Theory Revision Donald Rose

Using Domain Knowledge to Improve Inductive Learning Algorithms for Diagnosis Gerhard Friedrich, Wolfgang Nejdl

Using Learning to Recover Side-Effects of Operators in Robotics Ralph P. Sobek, Jean-Paul Laumond

Using Multiple Representations to Improve Inductive Bias: Gray and Binary Coding for Genetic Algorithms Rich Caruana, J. David Schaffer, Larry J. Eshelman

What Good Are Experiments? Ritchey A. Ruff, Thomas G. Dietterich