Machine Learning Proceedings 1992 - 1st Edition - ISBN: 9781558602472, 9781483298535

Machine Learning Proceedings 1992

1st Edition

Proceedings of the Ninth International Workshop (ML92)

Editors: Lawrence A. Birnbaum
eBook ISBN: 9781483298535
Imprint: Morgan Kaufmann
Published Date: 1st June 1992
Page Count: 448
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Description

Machine Learning: Proceedings of the Ninth International Workshop (ML92) covers the papers and posters presented at ML92, the Ninth International Machine Learning Conference, held at Aberdeen, Scotland on July 1-3, 1992. The book focuses on the advancements of practices, methodologies, approaches, and techniques in machine learning.

The selection first offers information on the principal axes method for constructive induction; learning by incomplete explanations of failures in recursive domains; and eliminating redundancy in explanation-based learning. Topics include means-ends analysis search in recursive domains, description space transformation, distance metric, generating similarity matrix, and learning principal axes. The text then examines trading off consistency and efficiency in version-space induction; improving path planning with learning; finding the conservation of momentum; and learning to predict in uncertain continuous tasks.

The manuscript elaborates on a teaching method for reinforcement learning, compiling prior knowledge into an explicit bias, spatial analogy and subsumption, and multistrategy learning with introspective meta-explanations. The publication also ponders on selecting typical instances in instance-based learning and temporal difference learning of backgammon strategy.

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

Table of Contents


Generalizing from Case Studies: A Case Study

On Learning More Concepts

The Principal Axes Method for Constructive Induction

Learning by Incomplete Explanations of Failures in Recursive Domains

Eliminating Redundancy in Explanation-Based Learning

Trading Off Consistency and Efficiency in Version-Space Induction

Peepholing: Choosing Attributes Efficiently for Megainduction

Improving Path Planning with Learning

The Right Representation for Discovery: Finding the Conservation of Momentum

Learning to Predict in Uncertain Continuous Tasks

Lazy Partial Evaluation: An Integration of Explanation-Based Generalisation and Partial Evaluation

A Teaching Method for Reinforcement Learning

Compiling Prior Knowledge into an Explicit Bias

Spatial Analogy and Subsumption

Learning to Satisfy Conjunctive Goals

Multistrategy Learning with Introspective Meta-Explanations

An Asymptotic Analysis of Speedup Learning

Why EBL Produces Overly-Specific Knowledge: A Critique of the PRODIGY Approaches

Automatic Feature Generation for Problem Solving Systems

Towards Inductive Generalisation in Higher Order Logic

Ordering Effects in Clustering

Learning Structured Concepts Using Genetic Algorithms

An Analysis of Learning to Plan as a Search Problem

An Approach to Anytime Learning

Artificial Universes—Towards a Systematic Approach to Evaluating Algorithms Which Learn from Examples

Average Case Analysis of Learning k-CNF Concepts

The MENTLE Approach to Learning Heuristics for the Control of Logic Programs

Fuzzy Substructure Discovery

Efficient Classification of Massive, Unsegmented Datastreams

Induction of One-Level Decision Trees

Combining Competition and Cooperation in Supervised Inductive Learning

A Practical Approach to Feature Selection

Learning as Optimization: Stochastic Generation of Multiple Knowledge

Dynamic Optimization

Sub-Unification: A Tool for Efficient Induction of Recursive Programs

Augmenting and Efficiently Utilizing Domain Theory in Explanation-Based Natural Language Acquisition

Enhancing Transfer in Reinforcement Learning by Building Stochastic Models of Robot Actions

THOUGHT: An Integrated Learning System for Acquiring Knowledge Structure

An Approach to Concept Learning Based on Term Generalization

Using Transitional Proximity for Faster Reinforcement Learning

NFDT: A System that Learns Flexible Concepts Based on Decision Trees for Numerical Attributes

A Symbolic Algorithm for Computing Coefficients' Accuracy in Regression

Compression, Significance, and Accuracy

Guiding Example Acquisition by Generating Scenarios

Constructive Induction Using a Non-Greedy Strategy for Feature Selection

Training Second-Order Recurrent Neural Networks Using Hints

DYNAMIC: A New Role for Training Problems in EBL

A Framework for Discovering Discrete Event Models

Learning Episodes for Optimization

Learning to Fly

Deconstructing the Digit Recognition Problem

On Combining Multiple Speedup Techniques

Scaling Reinforcement Learning Algorithms by Learning Variable Temporal Resolution Models

Detecting Novel Classes with Applications to Fault Diagnosis

Measuring Utility and the Design of Provably Good EBL Algorithms

Refining a Relational Theory with Multiple Faults in the Concept and Subconcepts

Cooperation in Knowledge Base Refinement

Temporal Difference Learning of Backgammon Strategy

AGIL: Solving the Exploration Versus Exploitation Dilemma in a Simple Classifier System Applied to Simulated Robotics

Conceptual Clustering with Systematic Missing Values

Selecting Typical Instances in Instance-Based Learning

The First Phase of Real-World Discovery: Determining Repeatability and Error of Experiments

Author Index

Subject Index

Details

No. of pages:
448
Language:
English
Copyright:
© Morgan Kaufmann 1992
Published:
Imprint:
Morgan Kaufmann
eBook ISBN:
9781483298535

About the Editor

Lawrence A. Birnbaum