Can I read Adaptive Learning of Polynomial Networks: Genetic Programming, Backpropagation and Bayesian Methods (Genetic and Evolutionary Computation) on EtoBox?

Adaptive Learning of Polynomial Networks: Genetic Programming, Backpropagation and Bayesian Methods (Genetic and Evolutionary Computation) by Nikolay Y. Nikolaev, Hitoshi Iba (auth.) is a nonfiction available to read on EtoBox.

What is Adaptive Learning of Polynomial Networks: Genetic Programming, Backpropagation and Bayesian Methods (Genetic and Evolutionary Computation) about?

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). The book 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 ar

Who reads Adaptive Learning of Polynomial Networks: Genetic Programming, Backpropagation and Bayesian Methods (Genetic and Evolutionary Computation)?

Author

Nikolay Y. Nikolaev, Hitoshi Iba (auth.)

Publisher

Springer Science+Business Media, Inc

Published

2006

Language

EN

ISBN

9786610613854

Category

nonfiction

Subjects

Computer Science, Science, Mathematics