Machine Learning Proceedings 1988

Machine Learning Proceedings 1988

1st Edition - December 25, 1988

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  • Editor: John Laird
  • eBook ISBN: 9781483297699

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Description

Proceedings of the Fifth International Conference on Machine Learning provides careful theoretical analyses that make clear contact with traditional problems in machine learning. This book discusses the key role of learning in cognition. Organized into 10 parts encompassing 49 chapters, this book begins with an overview of the OTIS induction system that learns concepts from positive and negative examples by searching through the space of possible concept descriptions. This text then reviews the methods to selecting examples, and explores the ramifications of one in detail. Other chapters consider a reported phenomenon in machine concept learning wherein concept descriptions can be simplified with little ill-effect on classification accuracy. This book discusses as well an implemented system that learns structural models of shape from noisy image data. The final chapter provides a discussion of the relationship between learning and forgetting. This book is a valuable resource for psychologists, scientists, theorists, and research workers.

Table of Contents


  • Empirical Learning

    Using a Generalization Hierarchy to Learn from Examples

    Tuning Rule-Based Systems to their Environments

    On Asking the Right Questions

    Concept Simplification and Prediction Accuracy

    Learning Graph Models of Shape

    Learning Categorical Decision Criteria in Biomedical Domains

    Conceptual Clumping of Binary Vectors with Occam's Razor

    AutoClass: A Bayesian Classification System

    Incremental Multiple Concept Learning Using Experiments

    Trading Off Simplicity and Coverage in Incremental Concept Learning

    Deferred Commitment in UNIMEM: Waiting to Learn

    Experiments on the Costs and Benefits of Windowing in ID3

    Improved Decision Trees: A Generalized Version of ID3

    ID5: An Incremental ID3

    Using Weighted Networks to Represent Classification Knowledge in Noisy Domains

    Genetic Learning

    An Empirical Comparison of Genetic and Decision-Tree Classifiers

    Population Size in Classifier Systems

    Representation and Hidden Bias: Gray vs. Binary Coding for Genetic Algorithms

    Classifier Systems with Hamming Weights

    Midgard: A Genetic Approach to Adaptive Load Balancing for Distributed Systems

    Connectionist Learning

    Some Interesting Properties of a Connectionist Inductive Learning System

    Competitive Reinforcement Learning

    Connectionist Learning of Expert Backgammon Evaluations

    Building and Using Mental Models in a Sensory-Motor Domain: A Connectionist Approach

    Explanation-Based Learning

    Reasoning about Operationality for Explanation-Based Learning

    Boundaries of Operationality

    On the Tractability of Learning from Incomplete Theories

    Active Explanation Reduction: An Approach to the Multiple Explanations Problem

    Generalizing Number and Learning from Multiple Examples in Explanation Based Learning

    Generalizing the Order of Operators in Macro-Operators

    Using Experience-Based Learning in Game Playing

    Integrated Explanation-Based and Empirical Learning

    Integrated Learning with Incorrect and Incomplete Theories

    An Approach Based on Integrated Learning to Generating Stories from Stories

    A Knowledge Intensive Approach to Concept Induction

    Case-Based Learning

    Learning to Program by Examining and Modifying Cases

    Machine Discovery

    Theory Discovery and the Hypothesis Language

    Machine Invention of First Order Predicates by Inverting Resolution

    The Interdependencies of Theory Formation, Revision, and Experimentation

    A Hill-Climbing Approach to Machine Discovery

    Reduction: A Practical Mechanism of Searching for Regularity in Data

    Formal Models of Concept Learning

    Extending the Valiant Learning Model

    Learning Systems of First-Order Rules

    Two New Frameworks for Learning

    Hypothesis Filtering: A Practical Approach to Reliable Learning

    Experimental Results in Machine Learning

    Diffy-S: Learning Robot Operator Schemata from Examples

    Experimental Results from an Evaluation of Algorithms that Learn to Control Dynamic Systems

    Utilizing Experience for Improving the Tactical Manager

    Computational Impact of Learning and Forgetting

    Some Chunks Are Expensive

    The Role of Forgetting in Learning

Product details

  • No. of pages: 467
  • Language: English
  • Copyright: © Morgan Kaufmann 1998
  • Published: December 25, 1988
  • Imprint: Morgan Kaufmann
  • eBook ISBN: 9781483297699

About the Editor

John Laird

Affiliations and Expertise

University of Michigan, Ann Arbor, U.S.A.

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