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Adaptive Learning of Polynomial Networks
Nikolaev, Nikolay; Iba, Hitoshi
€ 200.50
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Description for Adaptive Learning of Polynomial Networks
Hardback. Delivers theoretical and practical knowledge for developing algorithms that infer linear and non-linear multivariate models, providing a methodology for inductive learning of Polynomial Neural Network models (PNN) from data. This work emphasizes an organized identification process by which to discover models that generalize and predict well. Series: Genetic and Evolutionary Computation. Num Pages: 316 pages, biography. BIC Classification: UY. Category: (P) Professional & Vocational. Dimension: 235 x 156 x 19. Weight in Grams: 647.
This book provides theoretical and practical knowledge for develop ment of algorithms that infer linear and nonlinear models. It offers a methodology for inductive learning of polynomial neural network mod els from data. The design of such tools contributes to better statistical data modelling when addressing tasks from various areas like system identification, chaotic time-series prediction, financial forecasting and data mining. The main claim is that the model identification process involves several equally important steps: finding the model structure, estimating the model weight parameters, and tuning these weights with respect to the adopted assumptions about the underlying data distrib ution. ... Read more
This book provides theoretical and practical knowledge for develop ment of algorithms that infer linear and nonlinear models. It offers a methodology for inductive learning of polynomial neural network mod els from data. The design of such tools contributes to better statistical data modelling when addressing tasks from various areas like system identification, chaotic time-series prediction, financial forecasting and data mining. The main claim is that the model identification process involves several equally important steps: finding the model structure, estimating the model weight parameters, and tuning these weights with respect to the adopted assumptions about the underlying data distrib ution. ... Read more
Product Details
Format
Hardback
Publication date
2006
Publisher
Springer-Verlag New York Inc. United States
Number of pages
316
Condition
New
Series
Genetic and Evolutionary Computation
Number of Pages
316
Place of Publication
New York, NY, United States
ISBN
9780387312392
SKU
V9780387312392
Shipping Time
Usually ships in 15 to 20 working days
Ref
99-15
Reviews for Adaptive Learning of Polynomial Networks
From the reviews: "This book describes induction of polynomial neural networks from data. … This book may be used as a textbook for an advanced course on special topics of machine learning." (Jerzy W. Grzymala-Busse, Zentralblatt MATH, Vol. 1119 (21), 2007)