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Title Machine learning : from theory to applications : cooperative research at Siemens and MIT / S.J. Hanson, W. Remmele, R.L. Rivest, eds.

Published Berlin ; New York : Springer-Verlag, ©1993.

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Location Call No. Status
 UniM INTERNET resource    AVAILABLE
Physical description 1 online resource (viii, 271 pages) : illustrations.
Series Lecture notes in computer science, 0302-9743 ; 661
Lecture notes in computer science ; 661. 0302-9743
Springer Lecture Notes in Computer Science
Notes "This volume includes some of the key research papers in the area of machine learning produced at MIT and Siemens"--Preface.
Bibliography Includes bibliographical references and index.
Contents Strategic directions in machine learning / Stephen José Hanson, Werner Remmele and Ronald L. Rivest -- Training a 3-node neural network is NP-complete / Avrim L. Blum and Ronald L. Rivest -- Cryptographic limitations on learning Boolean formulae and finite automata / Michael J. Kearns and Leslie G. Valiant -- Inference of finite automata using homing sequences / Ronald L. Rivest and Robert E. Schapire -- Adaptive search by learning from incomplete explanations of failures / Neeraj Bhatnagar -- Learning of rules for fault diagnosis in power supply networks / R. Meunier, R. Scheiterer and A. Hech -- Cross references are features / Robert W. Schwanke and Michael A. Platoff -- The schema mechanism / Gary L. Drescher -- L-ATMS : a tight integration of EBL and the ATMS / Kai Zercher -- Massively parallel symbolic induction of protein structure/function relationships / Richard H. Lathrop [and others].
Task decomposition through competition in a modular connectionist architecture : the what and where vision tasks / Robert A. Jacobs, Michael I. Jordan and Andrew G. Barto -- Phoneme discrimination using connectionist networks / Raymond L. Watrous -- Behavior-based learning to control IR oven heating : preliminary investigations / R. Chou [and others] -- Trellis codes, receptive fields, and fault tolerant, self-repairing neural networks / Thomas Petsche and Bradley W. Dickinson.
Summary 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.
Other author Hanson, Stephen José.
Remmele, Werner.
Rivest, Ronald L.
SpringerLink issuing body.
Subject Machine learning -- Congresses.
Artificial intelligence -- Congresses.
Neural networks (Computer science) -- Congresses.
Electronic books.
Conference papers and proceedings.
ISBN 9783540475682
3540475680
0387564837
9780387564838
3540564837
9783540564836