Cambridge, MA: The MIT Press, 1994. Paperback. 500 pp.- These original contributions converge on an exciting and fruitful intersection of three historically distinct areas of learning research: computational learning theory, neural networks, and symbolic machine learning. Bridging theory and practice, computer science and psychology, they consider general issues in learning systems that could provide constraints for theory and at the same time interpret theoretical results in the context of experiments with actual learning systems. In all, nineteen chapters address questions such as, What is a natural system? How should learning systems gain from prior knowledge? If prior knowledge is important, how can we quantify how important? What makes a learning problem hard? How are neural networks and symbolic machine learning approaches similar? Is there a fundamental difference in the kind of task a neural network can easily solve as opposed to those a symbolic algorithm can easily solve? English text. Condition : as new. Condition : as new copy. ISBN 9780262581264. Keywords : ,

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Bibliografische Details

Titel: Computational Learning Theory and Natural Learning Systems, Vol. I: Constraints and Prospects.
Verlag: Bradford Books
Ver�ffentlichungsdatum: 1994
Sprache: Englisch
ISBN-10: 0262581264
ISBN-13: 9780262581264
Bindung: Softcover
Zustand: as new

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These original contributions converge on an exciting and fruitful intersection of three historically distinct areas of learning research: computational learning theory, neural networks, and symbolic machine learning. Bridging theory and practice, computer science and psychology, they consider general issues in learning systems that could provide constraints for theory and at the same time interpret theoretical results in the context of experiments with actual learning systems.

In all, nineteen chapters address questions such as, What is a natural system? How should learning systems gain from prior knowledge? If prior knowledge is important, how can we quantify how important? What makes a learning problem hard? How are neural networks and symbolic machine learning approaches similar? Is there a fundamental difference in the kind of task a neural network can easily solve as opposed to those a symbolic algorithm can easily solve?

Stephen J. Hanson heads the Learning Systems Department at Siemens Corporate Research and is a Visiting Member of the Research Staff and Research Collaborator at the Cognitive Science Laboratory at Princeton University. George A. Drastal is Senior Research Scientist at Siemens Corporate Research. Ronald J. Rivest is Professor of Computer Science and Associate Director of the Laboratory for Computer Science at the Massachusetts Institute of Technology.

�ber die Autorinnen und Autoren

George A. Drastal is Senior Research Scientist at Siemens Corporate Research.

Stephen Jos� Hanson is Professor of Psychology (Newark Campus) and Member of the Cognitive Science Center (New Brunswick Campus) at Rutgers University.

Ronald L. Rivest is Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology.

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