Machine Learning Proceedings 1993 - 1st Edition - ISBN: 9781558603073, 9781483298627

Machine Learning Proceedings 1993

1st Edition

Proceedings of the Tenth International Conference on Machine Learning, University of Massachusetts, Amherst, June 27-29, 1993

Editors: Unknown
eBook ISBN: 9781483298627
Imprint: Morgan Kaufmann
Published Date: 1st July 1993
Page Count: 540
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Machine Learning: Proceedings of the Tenth International Conference covers the papers presented at the Tenth International Conference on Machine Learning, held at Amherst, Massachusetts in June 27-29, 1993. The book focuses on the advancements of techniques, practices, approaches, and methodologies in machine learning.

The selection first offers information on automatic algorithm/model class selection, using decision trees to improve case-based learning, GALOIS, and multitask learning. Discussions focus on multitask connectionist learning in more detail; multitask decision trees; an algorithm for the incremental determination of the concept lattice; and empirical evaluation of GALOIS as a learning system. The text then examines the use of qualitative models to guide inductive learning; automation of path analysis for building causal models from data; and construction of hidden variables in Bayesian networks via conceptual clustering.

The book ponders on synthesis of abstraction hierarchies for constraint satisfaction by clustering approximately equivalent objects; efficient domain-independent experimentation; learning search control knowledge for deep space network scheduling; and learning procedures from interactive natural language instructions.

The selection is a dependable reference for researchers wanting to explore the field of machine learning.

Table of Contents


Organizing Committee and Program Committee



The Evolution of Genetic Algorithms: Towards Massive Parallelism

ÉLÉNA: A Bottom-Up Learning Method

Addressing the Selective Superiority Problem: Automatic Algorithm/Model Class Selection

Using Decision Trees to Improve Case-Based Learning

GALOIS: An Order-Theoretic Approach to Conceptual Clustering

Multitask Learning: A Knowledge-Based Source of Inductive Bias

Using Qualitative Models to Guide Inductive Learning

Automating Path Analysis for Building Causal Models from Data

Constructing Hidden Variables in Bayesian Networks Via Conceptual Clustering

Learning Symbolic Rules Using Artificial Neural Networks

Small Disjuncts in Action: Learning to Diagnose Errors in the Local Loop of the Telephone Network

Concept Sharing: A Means to Improve Multi-Concept Learning

Discovering Dynamics

Synthesis of Abstraction Hierarchies for Constraint Satisfaction by Clustering Approximately Equivalent Objects

SKICAT: A Machine Learning System for Automated Cataloging of Large Scale Sky Surveys

Learning From Entailment: An Application to Propositional Horn Sentences

Efficient Domain-Independent Experimentation

Learning Search Control Knowledge for Deep Space Network Scheduling

Learning Procedures from Interactive Natural Language Instructions

Generalization Under Implication by Recursive Anti-Unification

Supervised Learning and Divide-and-Conquer: A Statistical Approach

Hierarchical Learning in Stochastic Domains: Preliminary Results

Constraining Learning with Search Control

Scaling Up Reinforcement Learning for Robot Control

Overcoming Incomplete Perception with Utile Distinction Memory

Explanation Based Learning: A Comparison of Symbolic and Neural Network Approaches

Combinatorial Optimization in Inductive Concept Learning

Decision Theoretic Subsampling for Induction on Large Databases

Learning DNF Via Probabilistic Evidence Combination

Explaining and Generalizing Diagnostic Decisions

Combining Instance-Based and Model-Based Learning

Data Mining of Subjective Agricultural Data

Lookahead Feature Construction for Learning Hard Concepts

Adaptive NeuroControl: How Black Box and Simple can it be

An SE-Tree Based Characterization of the Induction Problem

Density-Adaptive Learning and Forgetting

Efficiently Inducing Determinations: A Complete and Systematic Search Algorithm that Uses Optimal Pruning

Compiling Bayesian Networks into Neural Networks

A Reinforcement Learning Method for Maximizing Undiscounted Rewards

ATM Scheduling with Queuing Delay Predictions

Online Learning with Random Representations

Learning from Queries and Examples with Tree-Structured Bias

Multi-Agent Reinforcement Learning: Independent Vs. Cooperative Agents

Better Learners Use Analogical Problem Solving Sparingly

Author Index

Subject Index


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© Morgan Kaufmann 1993
Morgan Kaufmann
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