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Computational Learning Theory and Natural Learning Systems Constraints and Prospects

Author: 


ISBN-13: 

9780262581264


ISBN: 

0262581264


Publisher:  MIT Press
Summary: 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 result...s 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.

Hanson, Stephen J. is the author of Computational Learning Theory and Natural Learning Systems Constraints and Prospects, published under ISBN 9780262581264 and 0262581264. Seven Computational Learning Theory and Natural Learning Systems Constraints and Prospects textbooks are available for sale on ValoreBooks.com, six used from the cheapest price of $26.59, or buy new starting at $99.87.
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Computational Learning Theory and Natural Learning Systems Constraints and Prospects

ISBN-13: 9780262581264


ISBN: 0262581264


Publisher: MIT Press
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