Machine Learning Proceedings 1989

Machine Learning Proceedings 1989

1st Edition - June 1, 1989

Write a review

  • Editor: Alberto Maria Segre
  • eBook ISBN: 9781483297408

Purchase options

Purchase options
DRM-free (PDF)
Sales tax will be calculated at check-out

Institutional Subscription

Free Global Shipping
No minimum order


Proceedings of the Sixth International Workshop on Machine Learning covers the papers presented at the Sixth International Workshop of Machine Learning, held at Cornell University, Ithaca, New York (USA) on June 26-27, 1989. The book focuses on the processes, methodologies, techniques, and approaches involved in machine learning. The selection first offers information on unifying themes in empirical and explanation-based learning; integrated learning of concepts with both explainable and conventional aspects; conceptual clustering of explanations; and tight integration of deductive and inductive learning. The text then examines multi-strategy learning in nonhomogeneous domain theories; description of preference criterion in constructive learning; and combining case-based reasoning, explanation-based learning, and learning from instruction. Discussions focus on causal explanation of actions, constructive learning, learning in a weak theory domain, learning problem, and individual criteria and their relationships. The book elaborates on learning from plausible explanations, augmenting domain theory for explanation-based generalization, reducing search and learning goal preferences, and using domain knowledge to improve inductive learning algorithms for diagnosis. The selection is a dependable reference for researchers interested in the dynamics of machine learning.

Table of Contents

  • Preface

    Combining Empirical and Explanation-Based Learning

    Unifying Themes in Empirical and Explanation-Based Learning

    Induction Over the Unexplained: Integrated Learning of Concepts with Both Explainable and Conventional Aspects

    Conceptual Clustering of Explanations

    A Tight Integration of Deductive and Inductive Learning

    Multi-Strategy Learning in Nonhomogeneous Domain Theories

    A Description of Preference Criterion in Constructive Learning: A Discussion of Basic Issues

    Combining Case-Based Reasoning, Explanation-Based Learning, and Learning from Instruction

    Deduction in Top-Down Inductive Learning

    One-Sided Algorithms for Integrating Empirical and Explanation-Based Learning

    Combining Empirical and Analytical Learning with Version Spaces

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

    Learning from Plausible Explanations

    Augmenting Domain Theory for Explanation-Based Generalisation

    Explanation Based Learning as Constrained Search

    Reducing Search and Learning Goal Preferences

    Adaptation-Based Explanation: Explanations as Cases

    A Retrieval Model Using Feature Selection

    Improving Decision-Making on the Basis of Experience

    Explanation-Based Acceleration of Similarity-Based Learning

    Knowledge Acquisition Planning: Results and Prospects

    "Learning by Instruction" in Connectionist Systems

    Integrating Learning in a Neural Network

    Explanation-Based Learning with Weak Domain Theories

    Using Domain Knowledge to Improve Inductive Learning Algorithms for Diagnosis

    A Framework for Improving Efficiency and Accuracy

    Error Correction in Constructive Induction

    Improving Explanation-Based Indexing with Empirical Learning

    A Schema for an Integrated Learning System

    Combining Explanation-Based Learning and Artificial Neural Networks

    Empirical Learning; Theory and Application

    Learning Classification Rules Using Bayes

    New Empirical Learning Mechanisms Perform Significantly Better in Real Life Domains

    Inductive Learning with BCT

    What Good Are Experiments?

    An Experimental Comparison of Human and Machine Learning Formalisms

    Two Algorithms That Learn DNF by Discovering Relevant Features

    Limitations on Inductive Learning

    The Induction of Probabilistic Rule Sets — The Itrule Algorithm

    Empirical Substructure Discovery

    Learning the Behavior of Dynamical Systems from Examples

    Experiments in Robot Learning

    Induction of Decision Trees from Inconclusive Data

    Knowledge Intensive Induction

    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

    Signal Detection Theory: Valuable Tools for Evaluating Inductive Learning

    Unknown Attribute Values in Induction

    Processing Issues in Comparisons of Symbolic and Connectionist Learning Systems

    Bacon, Data Analysis and Artificial Intelligence

    Learning Plan Knowledge

    Learning to Plan in Complex Domains

    An Empirical Analysis of EBL Approaches for Learning Plan Schemata

    Learning Decision Rules for Scheduling Problems: A Classifier Hybrid Approach

    Learning Tactical Plans for Pilot Aiding

    Issues in the Justification-Based Diagnosis of Planning Failures

    Learning Procedural Knowledge in the EBG Context

    Learning Invariants from Explanations

    Using Learning to Recover Side-Effects of Operators in Robotics

    Learning to Recognize Plans Involving Affect

    Learning to Retrieve Useful Information for Problem Solving

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

    Approximating Learned Search Control Knowledge

    Planning in Games Using Approximately Learned Macros

    Learning Approximate Plans for Use in the Real World

    Using Concept Hierarchies to Organize Plan Knowledge

    Conceptual Clustering of Mean-Ends Plans

    Learning Appropriate Abstractions for Planning in Formation Problems

    Discovering Admissible Search Heuristics by Abstracting and Optimizing

    Learning Hierarchies of Abstraction Spaces

    Learning from Opportunity

    Learning by Analyzing Fortuitous Occurrences

    Explanation-Based Learning of Reactive Operators

    On Becoming Reactive

    Knowledge-Base Refinement and Theory Revision

    Knowledge Base Refinement and Theory Revision

    Theory Formation by Abduction: Initial Results of a Case Study Based on the Chemical Revolution

    Using Domain Knowledge to Aid Scientific Theory Revision

    The Role of Experimentation in Scientific Theory Revision

    Exemplar-Based Theory Rejection: An Approach to the Experience Consistency Problem

    Controlling Search for the Consequences of New Information During Knowledge Integration

    Identifying Knowledge Base Deficiencies by Observing User Behavior

    Toward Automated Rational Reconstruction: A Case Study

    Discovering Mathematical Operator Definitions

    Imprecise Concept Learning Within a Growing Language

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

    Some Results on the Complexity of Knowledge-Base Refinement

    Knowledge Base Refinement as Improving an Incorrect, Inconsistent and Incomplete Domain Theory

    Incremental Learning

    Incremental Learning of Control Strategies with Genetic Algorithms

    Tower of Hanoi with Connectionist Networks: Learning New Features

    A Formal Framework for Learning in Embedded Systems

    A Role for Anticipation in Reactive Systems that Learn

    Uncertainty Based Selection of Learning Experiences

    Improved Training Via Incremental Learning

    Incremental Batch Learning

    Incremental Concept Formation with Composite Objects

    Using Multiple Representations to Improve Inductive Bias: Gray and Binary Coding for Genetic Algorithms

    Focused Concept Formation

    An Exploration Into Incremental Learning: The Influence System

    Incremental, Instance-Based Learning of Independent and Graded Concept Descriptions

    Cost-Sensitive Concept Learning of Sensor Use in Approach and Recognition

    Reducing Redundant Learning

    Incremental Clustering by Minimizing Representation Length

    Information Filters and Their Implementation in the SYLLOG System

    Adaptive Learning of Decision-Theoretic Search Control Knowledge

    Atoms of Learning II: Adaptive Strategies A Study of Two- Person Zero-Sum Competition

    An Incremental Genetic Algorithm for Real-Time Learning

    Participatory Learning: A Constructivist Model

    Representational Issues in Machine Learning

    Representational Issues in Machine Learning

    Labor Saving New Distinctions

    A Theory of Justified Reformulations

    Reformulation from State Space to Reduction Space

    Knowledge-Based Feature Generation

    Enriching Vocabularies by Generalizing Explanation Structures

    Higher-Order and Modal Logic as a Framework for Explanation-Based Generalization

    Towards a Formal Analysis of EBL

    A Mathematical Framework for Studying Representation

    Refining Representations to Improve Problem Solving Quality

    Comparing Systems and Analyzing Functions to Improve Constructive Induction

    Evaluating Alternative Instance Representations

    Evaluating Bias During Pac-Learning

    Constructing Representations Using Inverted Spaces

    A Constructive Induction Framework

    Constructive Induction by Analogy

    Concept Discovery Through Utilization of Invariance Embedded in the Description Language

    Declarative Bias for Structural Domains

    Automatic Construction of a Hierarchical Generate-and-Test Algorithm

    A Knowledge-Level Analysis of Informing

    An Object-Oriented Representation for Search Algorithms

    Compiling Learning Vocabulary from a Performance System Description

    Generalized Recursive Splitting Algorithms for Learning Hybrid Concepts

    Screening Hypotheses with Explicit Bias

    Building A Learning Bias from Perceived Dependencies

    A Bootstrapping Approach to Conceptual Clustering

    Overcoming Feature Space Bias in a Reactive Environment

    Author Index

Product details

  • No. of pages: 510
  • Language: English
  • Copyright: © Morgan Kaufmann 1989
  • Published: June 1, 1989
  • Imprint: Morgan Kaufmann
  • eBook ISBN: 9781483297408

About the Editor

Alberto Maria Segre

Ratings and Reviews

Write a review

There are currently no reviews for "Machine Learning Proceedings 1989"