Machine Learning: From Theory to Applications
Cooperative Research at Siemens and MIT
Published on 30. March 1993
Book
Paperback/Softback
VIII, 276 pages
978-3-540-56483-6 (ISBN)
Description
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.
More details
Series
Edition
1993 ed.
Language
English
Place of publication
Berlin
Germany
Publishing group
Springer Berlin
Target group
Professional and scholarly
Research
Illustrations
VIII, 276 p.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 16 mm
Weight
441 gr
ISBN-13
978-3-540-56483-6 (9783540564836)
DOI
10.1007/3-540-56483-7
Schweitzer Classification
Content
Strategic directions in machine learning.- Training a 3-node neural network is NP-complete.- Cryptographic limitations on learning Boolean formulae and finite automata.- Inference of finite automata using homing sequences.- Adaptive search by learning from incomplete explanations of failures.- Learning of rules for fault diagnosis in power supply networks.- Cross references are features.- The schema mechanism.- L-ATMS: A tight integration of EBL and the ATMS.- Massively parallel symbolic induction of protein structure/function relationships.- Task decomposition through competition in a modular connectionist architecture: The what and where vision tasks.- Phoneme discrimination using connectionist networks.- Behavior-based learning to control IR oven heating: Preliminary investigations.- Trellis codes, receptive fields, and fault tolerant, self-repairing neural networks.