0
Your cart

Your cart is empty

Books > Computing & IT > General theory of computing

Buy Now

Adaptive Learning of Polynomial Networks - Genetic Programming, Backpropagation and Bayesian Methods (Hardcover) Loot Price: R4,743
Discovery Miles 47 430
Adaptive Learning of Polynomial Networks - Genetic Programming, Backpropagation and Bayesian Methods (Hardcover): Nikolay...

Adaptive Learning of Polynomial Networks - Genetic Programming, Backpropagation and Bayesian Methods (Hardcover)

Nikolay Nikolaev, Hitoshi Iba

Series: Genetic and Evolutionary Computation

 (sign in to rate)
Loot Price R4,743 Discovery Miles 47 430 | Repayment Terms: R444 pm x 12*

Bookmark and Share

Expected to ship within 12 - 19 working days

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 willstrengthen 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.

General

Imprint: Springer-Verlag New York
Country of origin: United States
Series: Genetic and Evolutionary Computation
Release date: May 2006
First published: May 2006
Authors: Nikolay Nikolaev • Hitoshi Iba
Dimensions: 235 x 156 x 19mm (L x W x T)
Format: Hardcover
Pages: 316
ISBN-13: 978-0-387-31239-2
Categories: Books > Computing & IT > General theory of computing > General
Books > Computing & IT > Applications of computing > General
Promotions
LSN: 0-387-31239-0
Barcode: 9780387312392

Is the information for this product incomplete, wrong or inappropriate? Let us know about it.

Does this product have an incorrect or missing image? Send us a new image.

Is this product missing categories? Add more categories.

Review This Product

No reviews yet - be the first to create one!

Partners