Adaptive Learning of Polynomial Networks
Genetic Programming, Backpropagation and Bayesian Methods
Buch in deiner Nähe kaufen
Beschreibung
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 identification process by which to discover models that generalize and predict well. The investigations detailed here demonstrate that PNN models evolved by genetic programming and improved by backpropagation are successful when solving real-world tasks. Here is an essential reference for researchers and practitioners in the fields of evolutionary computation, artificial neural networks and Bayesian inference, as well for advanced-level students of genetic programming.
Offers a shift in focus from the standard linear models toward highly nonlinear models that can be inferred by contemporary learning approaches Presents alternative probabilistic search algorithms that discover the model architecture and neural network training techniques to find accurate polynomial weights Facilitates the discovery of polynomial models for time-series prediction Includes supplementary material: sn.pub/extras
Autor*in
Nikolay Nikolaev
Themen in »Adaptive Learning of Polynomial Networks«
Bayesian inference algorithms artificial intelligence genetic programming intelligence learning machine learning navigation programming
Stimmen zu »Adaptive Learning of Polynomial Networks«
From the reviews:
"This book describes induction of polynomial neural networks from data. … This book may be used as a textbook for an advanced course on special topics of machine learning." (Jerzy W. Grzymala-Busse, Zentralblatt MATH, Vol. 1119 (21), 2007)
()