Adaptive Learning of Polynomial Networks Genetic Programming, Backpropagation and Bayesian Methods
Heftet /
2011
/ Engelsk
Produktdetaljer
ISBN
9781441940605
Publisert
2011-02-11
Utgiver
Springer-Verlag New York Inc.
Høyde
235 mm
Bredde
155 mm
Aldersnivå
Research, P, 06
Språk
Product language
Engelsk
Format
Product format
Heftet
Antall sider
14
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)
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.
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Offers a shift in focus from the standard linear models toward highly nonlinear models that can be inferred by contemporary learning approaches Presents alternative probabilistic search algorithms that discover the model architecture and neural network training techniques to find accurate polynomial weights Facilitates the discovery of polynomial models for time-series prediction Includes supplementary material: sn.pub/extras
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Produktdetaljer
ISBN
9781441940605
Publisert
2011-02-11
Utgiver
Springer-Verlag New York Inc.
Høyde
235 mm
Bredde
155 mm
Aldersnivå
Research, P, 06
Språk
Product language
Engelsk
Format
Product format
Heftet
Antall sider
14