Foundations of Computational Intelligence: Volume 1: Learning and Approximation by Aboul-Ella Hassanien

Foundations of Computational Intelligence: Volume 1: Learning and Approximation

EditorAboul-Ella Hassanien, Ajith Abraham, Athanasios V. Vasilakos

Paperback | October 28, 2010

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Recent years have seen numerous applications across a variety of fields using various techniques of Computational Intelligence. This book, one of a series on the foundations of Computational Intelligence, is focused on learning and approximation.

Title:Foundations of Computational Intelligence: Volume 1: Learning and ApproximationFormat:PaperbackProduct dimensions:412 pages, 9.25 X 6.1 X 0 inShipping dimensions:412 pages, 9.25 X 6.1 X 0 inPublished:October 28, 2010Publisher:Springer Berlin HeidelbergLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:364210164X

ISBN - 13:9783642101649

Appropriate for ages: All ages

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Table of Contents

Part I Function Approximation.- Machine Learning and Genetic Regulatory Networks: A Review and a Roadmap.- Automatic Approximation of Expensive Functions with Active Learning.- New Multi-Objective Algorithms for Neural Network Training applied to Genomic Classification Data.- An Evolutionary Approximation for the Coefficients of Decision Functions within a Support Vector Machine Learning Strategy.- Part II Connectionist Learning.- Meta-learning and Neurocomputing - A New Perspective for Computational Intelligence.- Three-term Fuzzy Back-propagation.- Entropy Guided Transformation Learning.- Artificial Development.- Robust Training of Artificial Feed-forward Neural Networks.- Workload Assignment In Production Networks By Multi-Agent Architecture.- Part III Knowledge Representation and Acquisition.- Extensions to Knowledge Acquisition and Effect of Multimodal Representation in Unsupervised Learning.- A New Implementation for Neural Networks in Fourier-Space.- Part IV Learning and Visualization.- Dissimilarity Analysis and Application to Visual Comparisons.- Dynamic Self-Organising Maps: Theory, Methods and Applications.- Hybrid Learning Enhancement of RBF Network with Particle Swarm Optimization.