Buch, Englisch, Band 201, 400 Seiten, Previously published in hardcover, Format (B × H): 155 mm x 235 mm, Gewicht: 622 g
Volume 1: Learning and Approximation
Buch, Englisch, Band 201, 400 Seiten, Previously published in hardcover, Format (B × H): 155 mm x 235 mm, Gewicht: 622 g
Reihe: Studies in Computational Intelligence
ISBN: 978-3-642-10164-9
Verlag: Springer
First volume of a Reference work on the foundations of computational intelligence Devoted to learning and approximation
Zielgruppe
Research
Autoren/Hrsg.
Weitere Infos & Material
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.
