Description: Adaptive Learning of Polynomial Networks : Genetic Programming, Backpropagation and Bayesian Methods, Paperback by Nikolaev, Nikolay; Iba, Hitoshi, ISBN 144194060X, ISBN-13 9781441940605, Brand New, Free shipping in the US 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. When the learning process is organized according to these steps, performed together one after the other or separately, one may expect to discover models that generalize well (that is, predict well). Th off'ers statisticians a shift in focus from the standard f- ear models toward highly nonlinear models that can be found by con temporary learning approaches. Speciafists in statistical learning will read about alternative probabilistic search algorithms that discover the model architecture, and neural network training techniques that identify accurate polynomial weights. They wfil be pleased to find out that the discovered models can be easily interpreted, and these models assume statistical diagnosis by standard statistical means. Covering the three fields of: evolutionary computation, neural net works and Bayesian inference, orients th to a large audience of researchers and practitioners.
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Book Title: Adaptive Learning of Polynomial Networks : Genetic Programming, B
Number of Pages: Xiv, 316 Pages
Language: English
Publication Name: Adaptive Learning of Polynomial Networks : Genetic Programming, Backpropagation and Bayesian Methods
Publisher: Springer
Publication Year: 2011
Subject: Programming / Algorithms, Intelligence (Ai) & Semantics, Computer Science, Neural Networks, Algebra / General, Probability & Statistics / Bayesian Analysis
Type: Textbook
Item Weight: 18 Oz
Author: Nikolay Nikolaev, Hitoshi Iba
Subject Area: Mathematics, Computers
Item Length: 9.3 in
Item Width: 6.1 in
Series: Genetic and Evolutionary Computation Ser.
Format: Trade Paperback
