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Adaptive Learning of Polynomial Networks

Name: Adaptive Learning of Polynomial Networks
Brand: Springer-Verlag New York Inc.
SKU: 9781441940605
Price: 747.91 RON
Availability: InStock

Genetic Programming, Backpropagation and Bayesian Methods

Autor: Nikolay Nikolaev, Hitoshi Iba

Limbă:

Legare: Carte broșată

Editura: Springer-Verlag New York Inc.

Disponibilitate: În depozitul extern

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747.91 lei

This book delivers theoretical and practical knowledge for developing algorithms that infer linear a...

Informații despre carte

Autor

Nikolay Nikolaev, Hitoshi Iba

Limbă

engleză

Legare

Carte - Carte broșată

Publicat

2011

Pagini

316

EAN

9781441940605

ISBN

144194060X

Enbook ID

01422587

Editura

Springer-Verlag New York Inc.

Greutate

510

Dimensiuni

155 x 235 x 19

Descriere completă

This book 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. The text emphasizes an organized model identification process by which to discover models that generalize and predict well. The book further facilitates the discovery of polynomial models for time-series prediction.Adaptive Learning of Polynomial Networks delivers theoretical and practical knowledge for the development of algorithms that infer linear and non-linear multivariate models, providing a methodology for inductive learning of polynomial neural network models (PNN) from data. The empirical investigations detailed here demonstrate that PNN models evolved by genetic programming and improved by backpropagation are successful when solving real-world tasks. §The text emphasizes the model identification process and presents§a shift in focus from the standard linear models toward highly nonlinear models that can be inferred by contemporary learning approaches,§alternative probabilistic search algorithms that discover the model architecture and neural network training techniques to find accurate polynomial weights,§a means of discovering polynomial models for time-series prediction, and §an exploration of the areas of artificial intelligence, machine learning, evolutionary computation and neural networks, covering definitions of the basic inductive tasks, presenting basic approaches for addressing these tasks, introducing the fundamentals of genetic programming, reviewing the error derivatives for backpropagation training, and explaining the basics of Bayesian learning.§This volume is an essential reference for researchers and practitioners interested in the fields of evolutionary computation, artificial neural networks and Bayesian inference, and will also appeal to postgraduate and advanced undergraduate students of genetic programming. Readers will strengthen their skills in creating both efficient model representations and learning operators that efficiently sample the search space, navigating the search process through the design of objective fitness functions, and examining the search performance of the evolutionary system.