Produktbild: Foundations of Computational Intelligence
Band 201

Foundations of Computational Intelligence Volume 1: Learning and Approximation

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Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

06.05.2009

Abbildungen

XII, 400 p.

Herausgeber

Aboul-Ella Hassanien + weitere

Verlag

Springer Berlin

Seitenzahl

400

Maße (B/H)

15,5/23,5 cm

Gewicht

766 g

Sprache

Englisch

ISBN

978-3-642-01081-1

Beschreibung

Portrait

Dr. Ajith Abraham is Director of the Machine Intelligence Research (MIR) Labs, a global network of research laboratories with headquarters near Seattle, WA, USA. He is an author/co-author of more than 750 scientific publications. He is founding Chair of the International Conference of Computational Aspects of Social Networks (CASoN), Chair of IEEE Systems Man and Cybernetics Society Technical Committee on Soft Computing (since 2008), and a Distinguished Lecturer of the IEEE Computer Society representing Europe (since 2011).
Dr. Aboul-Ella Hassanien is a Professor in the Faculty of Computers and Information at Cairo University, Egypt, and Visiting Professor at the College of Business Administration, Kuwait University.
Dr. Aboul-Ella Hassanien is a Professor in the Faculty of Computers and Information at Cairo University, Egypt, and Visiting Professor at the College of Business Administration, Kuwait University.

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

06.05.2009

Abbildungen

XII, 400 p.

Herausgeber

Verlag

Springer Berlin

Seitenzahl

400

Maße (B/H)

15,5/23,5 cm

Gewicht

766 g

Sprache

Englisch

ISBN

978-3-642-01081-1

Herstelleradresse

Springer-Verlag GmbH
Heidelberger Platz 3
14197 Berlin
Deutschland
Email: sdc-bookservice@springer.com
Url: www.springer.com
Telephone: +49 30 827870
Fax: +49 30 8214091

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  • Produktbild: Foundations of Computational Intelligence
  • 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.- 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 Feedforward Neural Networks.- Workload Assignment in Production Networks by Multi Agent Architecture.- 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.- 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.