Computational Intelligence Paradigms

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Format: Hardcover
Pub. Date: 2008-08-03
Publisher(s): Springer Verlag
List Price: $179.99

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Summary

System designers are faced with a large set of data which has to be analysed and processed efficiently. Advanced computational intelligence paradigms present tremendous advantages by offering capabilities such as learning, generalisation and robustness. These capabilities help in designing complex systems which are intelligent and robust.The book includes a sample of research on the innovative applications of advanced computational intelligence paradigms. The characteristics of computational intelligence paradigms such as learning, generalization based on learned knowledge, knowledge extraction from imprecise and incomplete data are the extremely important for the implementation of intelligent machines. The chapters include architectures of computational intelligence paradigms, knowledge discovery, pattern classification, clusters, support vector machines and gene linkage analysis. We believe that the research on computational intelligence will simulate great interest among designers and researchers of complex systems. It is important to use the fusion of various constituents of computational intelligence to offset the demerits of one paradigm by the merits of another.

Table of Contents

An Introduction to Computational Intelligence Paradigmsp. 1
A Quest for Adaptable and Interpretable Architectures of Computational Intelligencep. 25
MembershipMap: A Data Transformation for Knowledge Discovery Based on Granulation and Fuzzy Membership Aggregationp. 51
Advanced Developments and Applications of the Fuzzy ARTMAP Neural Network in Pattern Classificationp. 77
Large Margin Methods for Structured Output Predictionp. 109
Ensemble MLP Classifier Designp. 133
Functional Principal Points and Functional Cluster Analysisp. 149
Clustering with Size Constraintsp. 167
Cluster Validating Techniques in the Presence of Duplicatesp. 181
Fuzzy Blocking Regression Modelsp. 195
Support Vector Machines and Features for Environment Perception in Mobile Roboticsp. 219
Linkage Analysis in Genetic Algorithmsp. 251
Author Indexp. 281
Table of Contents provided by Blackwell. All Rights Reserved.

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