Terrence L. Fine

Feedforward Neural Network Methodology (eBook, PDF)

• Format: PDF

Produktbeschreibung

This decade has seen an explosive growth in computational speed and memory and a rapid enrichment in our understanding of artificial neural networks. These two factors provide systems engineers and statisticians with the ability to build models of physical, economic, and information-based time series and signals. This book provides a thorough and coherent introduction to the mathematical properties of feedforward neural networks and to the intensive methodology which has enabled their highly successful application to complex problems.

---

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Produktdetails

• Verlag: Springer New York
• Seitenzahl: 340
• Erscheinungstermin: 6. April 2006
• Englisch
• ISBN-13: 9780387226491
• Artikelnr.: 43999164

Inhaltsangabe

Background and Organization.- Perceptrons - Networks with a Single Node.- Feedforward Networks I: Generalities and LTU Nodes.- Feedforward Networks II: Real-valued Nodes.- Algorithms for Designing Feedforward Networks.- Architecture Selection and Penalty Terms.- Generalization and Learning.

Rezensionen

From the reviews: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION "...Fine must be congratulated for a coherent presentation of carefully selected material. Given the diversity of the field, this represented a serious challenge. Again, Feeforward Neural Network Methodlogy is an excellent reference for whoever wants to be brought to the frontier of research. I enthusiastically recommend it."