Machine Learning 1990 International Conference: Proceedings of the Seventh International Conference (1990), University of Texas, Austin, Texas, June 21-23, 1990

Porter, Bruce (Editor)

ISBN 10: 1558601414 / ISBN 13: 9781558601413

Published by Morgan Kaufmann, 1990

About this Item

illustrated edition edition. 427 pages. 10.87x8.50x0.79 inches. In Stock.

Seller Inventory # zk1558601414

Bibliographic Details

• Title: Machine Learning 1990 International Conference: Proceedings of the Seventh International Conference (1990), University of Texas, Austin, Texas, June 21-23, 1990
• Publisher: Morgan Kaufmann
• Publication Date: 1990
• Language: English
• ISBN 10: 1558601414
• ISBN 13: 9781558601413
• Binding: Paperback
• Condition: Brand New
• Weight: 1.02 kilograms

About this title

Synopsis

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.