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.
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