Adaptive Learning of Polynomial Networks: Genetic Programming, Backpropagation and Bayesian Methods: Genetic and Evolutionary Computation
Autor Nikolay Nikolaev, Hitoshi Ibaen Limba Engleză Hardback – 3 mai 2006
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Specificații
ISBN-13: 9780387312392
ISBN-10: 0387312390
Pagini: 316
Ilustrații: XIV, 316 p.
Dimensiuni: 156 x 235 x 23 mm
Greutate: 0.7 kg
Ediția:2006
Editura: Springer Us
Colecția Springer
Seria Genetic and Evolutionary Computation
Locul publicării:New York, NY, United States
ISBN-10: 0387312390
Pagini: 316
Ilustrații: XIV, 316 p.
Dimensiuni: 156 x 235 x 23 mm
Greutate: 0.7 kg
Ediția:2006
Editura: Springer Us
Colecția Springer
Seria Genetic and Evolutionary Computation
Locul publicării:New York, NY, United States
Public țintă
ResearchCuprins
Inductive Genetic Programming.- Tree-Like PNN Representations.- Fitness Functions and Landscapes.- Search Navigation.- Backpropagation Techniques.- Temporal Backpropagation.- Bayesian Inference Techniques.- Statistical Model Diagnostics.- Time Series Modelling.- Conclusions.
Recenzii
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)
"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)
Caracteristici
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