Machine Learning: From Theory to Applications: Cooperative Research at Siemens and MIT (Lecture Notes in Computer Science, Band 661) Cooperative Research at Siemens and MIT

Hanson, Stephen J., Werner Remmele and Ronald L. Rivest

ISBN 10: 3540564837 / ISBN 13: 9783540564836

Published by Springer 13.06.2008., 2008

About this Item

288 Seiten Exemplar aus einer wissenchaftlichen Bibliothek Altersfreigabe FSK ab 0 Jahre Sprache: Englisch Gewicht in Gramm: 969 23,5 x 1,5 x 1,7 cm, Taschenbuch.

Seller Inventory # 401888

Bibliographic Details

• Title: Machine Learning: From Theory to Applications: Cooperative Research at Siemens and MIT (Lecture Notes in Computer Science, Band 661) Cooperative Research at Siemens and MIT
• Publisher: Springer 13.06.2008.
• Publication Date: 2008
• Language: English
• ISBN 10: 3540564837
• ISBN 13: 9783540564836
• Binding: Soft cover
• Edition: Auflage: 1993.
• Condition: Gut
• Weight: 969 grams
• Seller catalogs:
  • Alle Bücher

About this title

Synopsis

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.