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Machine Learning Proceedings 1990 - 1st Edition - ISBN: 9781558601413, 9781483298580

Machine Learning Proceedings 1990

1st Edition

Proceedings of the Seventh International Conference on Machine Learning, University of Texas, Austin, Texas, June 21-23 1990

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Editors: Bruce Porter Raymond Mooney
eBook ISBN: 9781483298580
Imprint: Morgan Kaufmann
Published Date: 1st June 1990
Page Count: 427
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Machine Learning: Proceedings of the Seventh International Conference (1990) covers the research results from 12 disciplines of machine learning represented at the Seventh International Conference on Machine Learning, held on June 21-23, 1990 at the University of Texas in Austin. The book focuses on the progress in the interest in machine learning, including methodologies, approaches, and techniques.

The selection first offers information on knowledge acquisition from examples using maximal representation learning, performance analysis of a probabilistic inductive learning system, and a comparative study of ID3 and backpropagation for English text-to-speech mapping. The text then examines learning from data with bounded inconsistency, improving fit-and-split algorithms, and an incremental method for finding multivariate splits for decision trees. Topics include issues for decision-tree induction, learning and approximation, conceptual-set-covering algorithm, bounded inconsistency, implementation, and examples of incremental processes.

The publication ponders on incremental induction of topologically minimal trees, rational analysis of categorization, search control, utility, and concept induction, graph clustering and model learning by data compression, and an analysis of representation shift in concept learning. Learning procedures by environment-driven constructive induction and improving the performance of genetic algorithms in automated discovery of parameters are also discussed.

The selection is a valuable source of data for researchers interested in machine learning.

Table of Contents

"Chapter 1 Empirical Learning

Knowledge Acquisition from Examples Using Maximal Representation Learning

KBG: A Knowledge Based Generalizer

Performance Analysis of A Probabilistic Inductive Learning System

A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping

Learning from Data with Bounded Inconsistency

Conceptual Set Covering: Improving Fit-And-Split Algorithms

Incremental Learning of Rules and Meta-Rules

An Incremental Method for Finding Multivariate Splits for Decision Trees

Incremental Induction of Topologically Minimal Trees

Chapter 2 Conceptual Clustering

A Rational Analysis of Categorization

Search Control, Utility, and Concept Induction

Graph Clustering and Model Learning by Data Compression

Chapter 3 Constructive Induction and Reformulation

An Analysis of Representation Shift In Concept Learning

Learning Procedures by Environment-Driven Constructive Induction

Beyond Inversion of Resolution

Chapter 4 Genetic Algorithms

Genetic Programming

Improving the Performance of Genetic Algorithms in Automated Discovery of Parameters

Using Genetic Algorithms to Learn Disjunctive Rules from Examples


Chapter 5 Neural Network & Reinforcement Learning

Learning Functions in k-DNF from Reinforcement

Is Learning Rate a Good Performance Criterion for Learning?

Active Perception and Reinforcement Learning

Chapter 6 Learning and Planning

Learning Plans for Competitive Domains

Explanations of Empirically Derived Reactive Plans

Learning and Enforcement: Stabilizing Environments to Facilitate Activity

Simulation-Assisted Learning by Competition: Effects of Noise Differences Between Training Model and Target Environment

Integrated Architecture for Learning, Planning, and Reacting Based on Approximating Dynamic Programming

Chapter 7 Robot Learning

Reducing Real-World Failures of Approximate Explanation-Based Rules

Correcting and Extending Domain Knowledge Using Outside Guidance

Acquisition of Dynamic Control Knowledge for a Robotic Manipulator

Feature Extraction and Clustering of Tactile Impressions with Connectionist Models

Chapter 8 Explanation-Based Learning

Generalizing the Order of Goals as an Approach to Generalizing Number

Learning Approximate Control Rules of High Utility

Applying Abstraction and Simplification to Learn in Intractable Domains

Explanation-Based Learning with Incomplete Theories: A Three-Step Approach

Using Abductive Recovery of Failed Proof s for Problem Solving by Analogy

Issues in the Design of Operator Composition Systems

Incremental Learning of Explanation Patterns and Their Indices

Chapter 9 Explanation-Based and Empirical Learning

Integrated Learning in a Real Domain

Incremental Version-Space Merging

Average Case Analysis of Conjunctive Learning Algorithms

ILS: A Framework for Multi-Paradigmatic Learning

An Integrated Framework of Inducing Rules from Examples

Chapter 10 Language Learning

Adaptive Parsing: A General Method for Learning Idiosyncratic Grammars

A Comparison of Learning Techniques in Second Language Learning

Learning String Patterns and Tree Patterns from Examples

Learning with Discrete Multi-Valued Neurons

Chapter 11 Other Topics

The General Utility Problem in Machine Learning

A Robust Approach to Numeric Discovery

More Results on the Complexity of Knowledge Base Refinement: Belief Networks



No. of pages:
© Morgan Kaufmann 1990
1st June 1990
Morgan Kaufmann
eBook ISBN:

About the Editors

Bruce Porter

Affiliations and Expertise

University of Texas at Austin, USA

Raymond Mooney

Ratings and Reviews