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?

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 d

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

Kluwer Academic Publishers

Published

2006

Language

EN

ISBN

9780387264172

Category

nonfiction

Subjects

Science, Computer Science, Management