Buch, Englisch, 262 Seiten, Paperback, Format (B × H): 155 mm x 235 mm, Gewicht: 429 g
Buch, Englisch, 262 Seiten, Paperback, Format (B × H): 155 mm x 235 mm, Gewicht: 429 g
Reihe: Perspectives in Neural Computing
ISBN: 978-1-4612-8469-7
Verlag: Springer
Zielgruppe
Research
Autoren/Hrsg.
Weitere Infos & Material
1 Introduction.- 2 Preliminaries of Information Theory and Neural Networks.- 2.1 Elements of Information Theory.- 2.2 Elements of the Theory of Neural Networks.- I: Unsupervised Learning.- 3 Linear Feature Extraction: Infomax Principle.- 4 Independent Component Analysis: General Formulation and Linear Case.- 5 Nonlinear Feature Extraction: Boolean Stochastic Networks.- 6 Nonlinear Feature Extraction: Deterministic Neural Networks.- II: Supervised Learning.- 7 Supervised Learning and Statistical Estimation.- 8 Statistical Physics Theory of Supervised Learning and Generalization.- 9 Composite Networks.- 10 Information Theory Based Regularizing Methods.- References.