Machine Learning Proceedings 1989
- 1st Edition - June 1, 1989
- Imprint: Morgan Kaufmann
- Editor: Alberto Maria Segre
- Language: English
- Paperback ISBN:9 7 8 - 1 - 5 5 8 6 0 - 0 3 6 - 2
- eBook ISBN:9 7 8 - 1 - 4 8 3 2 - 9 7 4 0 - 8
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,… Read more
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Request a sales quoteProceedings 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.
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
- Edition: 1
- Published: June 1, 1989
- No. of pages (eBook): 510
- Imprint: Morgan Kaufmann
- Language: English
- Paperback ISBN: 9781558600362
- eBook ISBN: 9781483297408
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