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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.
"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
Improving the Performance of Genetic Algorithms in Automated Discovery of Parameters
Using Genetic Algorithms to Learn Disjunctive Rules from Examples
NEWBOOLE: A Fast GBML System
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:
University of Texas at Austin, USA
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