Machine Learning: From Theory to Applications - Cooperative Research at Siemens and MIT (Paperback, 1993 ed.)

This volume includes some of the key research papers in the area of machine learning produced at MIT and Siemens during a three-year joint research effort. It includes papers on many different styles of machine learning, organized into three parts. Part I, theory, includes three papers on theoretical aspects of machine learning. The first two use the theory of computational complexity to derive some fundamental limits on what isefficiently learnable. The third provides an efficient algorithm for identifying finite automata. Part II, artificial intelligence and symbolic learning methods, includes five papers giving an overview of the state of the art and future developments in the field of machine learning, a subfield of artificial intelligence dealing with automated knowledge acquisition and knowledge revision. Part III, neural and collective computation, includes five papers sampling the theoretical diversity and trends in the vigorous new research field of neural networks: massively parallel symbolic induction, task decomposition through competition, phoneme discrimination, behavior-based learning, and self-repairing neural networks.

General

Imprint:	Springer-Verlag
Country of origin:	Germany
Series:	Lecture Notes in Computer Science, 661
Release date:	March 1993
First published:	1993
Editors:	Stephen Jose Hanson • Werner Remmele • Ronald L. Rivest
Dimensions:	233 x 155 x 15mm (L x W x T)
Format:	Paperback
Pages:	276
Edition:	1993 ed.
ISBN-13:	978-3-540-56483-6
Categories:	Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
LSN:	3-540-56483-7
Barcode:	9783540564836

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