Computational Intelligence Paradigms: Innovative Applications

Lakhmi C. Jain, Shing Chiang Tan, Chee Peng Lim (auth.), Lakhmi C. Jain, Mika Sato-Ilic, Maria Virvou, George A. Tsihrintzis, Valentina Emilia Balas, Canicious Abeynayake (eds.)3540794735, 9783540794738

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 :
Front Matter….Pages –
An Introduction to Computational Intelligence Paradigms….Pages 1-23
A Quest for Adaptable and Interpretable Architectures of Computational Intelligence….Pages 25-49
MembershipMap: A Data Transformation for Knowledge Discovery Based on Granulation and Fuzzy Membership Aggregation….Pages 51-76
Advanced Developments and Applications of the Fuzzy ARTMAP Neural Network in Pattern Classification….Pages 77-107
Large Margin Methods for Structured Output Prediction….Pages 109-132
Ensemble MLP Classifier Design….Pages 133-147
Functional Principal Points and Functional Cluster Analysis….Pages 149-165
Clustering with Size Constraints….Pages 167-180
Cluster Validating Techniques in the Presence of Duplicates….Pages 181-193
Fuzzy Blocking Regression Models….Pages 195-217
Support Vector Machines and Features for Environment Perception in Mobile Robotics….Pages 219-250
Linkage Analysis in Genetic Algorithms….Pages 251-279
Back Matter….Pages –