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

By Stephen José Hanson

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?

• Format: Paperback
• Language: English
• ISBN: 0262581264
• ISBN13: 9780262581264
• Release Date: April 1994
• Publisher: Bradford Book
• Length: 577 Pages

Customer Reviews

well

it was my term homework which i prefer then the other units.And i m very glad for choseing this book cause it s out of boring classic managment units. It gives me diffrent management dimension. sorry about my english