From:
http://www.springer.com/west/home/computer/artificial?SGWID=4-147-22-150955424-0
Adaptive Learning of Polynomial Networks
Genetic Programming, Backpropagation and Bayesian Methods
Series: Genetic and Evolutionary Computation
Nikolaev, Nikolay, Iba, Hitoshi
2006, XIV, 316 p., Hardcover
About this book
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.
Written for:
Researchers and professionals in Genetic Programming and Statistical Learning
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