
Computational Learning Theory and Natural Learning Systems: Volume 1
Constraints and Prospects
MIT Press
Published on 10. April 1994
Book
Paperback/Softback
577 pages
978-0-262-58126-4 (ISBN)
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?
More details
Series
Language
English
Place of publication
Cambridge, Mass.
United States
Publishing group
MIT Press Ltd
Target group
College/higher education
Professional and scholarly
US School Grade: From College Freshman to College Graduate Student
Product notice
Paperback (trade)
Dimensions
Height: 226 mm
Width: 152 mm
Thickness: 33 mm
Weight
885 gr
ISBN-13
978-0-262-58126-4 (9780262581264)
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Schweitzer Classification