Computational Learning Theory and Natural Learning Systems: Volume 1 : Constraints and Prospects

Computational Learning Theory and Natural Learning Systems: Volume 1 : Constraints and Prospects

Description

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?


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Details

Author(s)
George Drastal, Stephen Jose Hanson, Ronald L. Rivest
Format
Paperback | 577 pages
Dimensions
152 x 226 x 33mm | 885g
Publication date
10 Apr 1994
Publisher
MIT Press Ltd
Imprint
Bradford Books
Publication City/Country
Massachusetts, United States
Language
English
ISBN10
0262581264
ISBN13
9780262581264