Machine Learning Proceedings 1990

Machine Learning Proceedings 1990 by Machine Learning epub pdf fb2

Title: Machine Learning Proceedings 1990
Author: Machine Learning
ISBN10: 1558601414
ISBN13: 978-1558601413
Publisher: Morgan Kaufmann; illustrated edition edition (June 15, 1990)
Language: English
Subcategory: Computer Science
Size PDF: 1409 kb
Size Fb2: 1723 kb
Rating: 4.7/5
Votes: 460
Pages: 427 pages
Other Format: docx txt doc mbr

Machine Learning Proceedings 1990 by Machine Learning

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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.

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