New Learning Paradigms in Soft Computing

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Springer Science & Business Media, 2002 - Computers - 464 pages
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Learning is a key issue in the analysis and design of all kinds of intelligent systems. In recent time many new paradigms of automated (machine) learning have been proposed in the literature. Soft computing, that has proved to be an effective and efficient tool in so many areas of science and technology, seems to offer new qualities in the realm of machine learning too. The purpose of this volume is to present some new learning paradigms that have been triggered, or at least strongly influenced by soft computing tools and techniques, mainly related to neural networks, fuzzy logic, rough sets, and evolutionary computations.
 

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Contents

Statistical Learning by Natural Gradient Descent
1
Granular Networks and Granular Learning
30
Learning and DecisionMaking in the Framework of Fuzzy Lattices
55
A Logical Method for Supervised Learning
97
Active Learning in Neural Networks
137
Knowledge Extraction from Reinforcement Learning
170
Application to a Pighouse Environment Control
181
A Critical Assessment of the Methodology
231
Digital Systems Design Through Learning
251
An Overview of CLIP Algorithms
276
An Integer Programming Approach to Inductive Learning Using Genetic and Greedy Algorithms
323
Using Unlabeled Data for Learning Classification Problems
368
Problems of Rule Induction from Preterm Birth Data
404
Reduction of Discriminant Rules Based on Frequent Item Set Calculation
419
Deriving a Concise Description of NonSelf Patterns in an Artificial Immune System
439
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About the author (2002)

Jain is director/founder of the Knowledge-Based Intelligent Engineering Systems Centre, located in the Division of Information Technology, Engineering and the Envvironment. He is a fellow of the Institution of Engineers, Australia.

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