- author = "Nikolay Nikolaev and Hitoshi Iba",
- title = "Adaptive Learning of Polynomial Networks Genetic Programming, Backpropagation and {Bayesian} Methods",
- publisher = "Springer",
- year = "2006",
- number = "4",
- series = "Genetic and Evolutionary Computation",
- note = "June",
- keywords = "genetic algorithms, genetic programming, Bayesian inference, adaptive learning, artificial intelligence, backpropagation, evolutionary computation, polynomial networks, time-series prediction",
- ISBN = "0-387-31239-0",
-
URL = "
http://www.springer.com/computer/ai/book/978-0-387-31239-2",
-
abstract = "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 emphasises an
organised model identification process by which to
discover models that generalise and predict well. The
empirical investigations detailed here demonstrate that
PNN models evolved by genetic programming and improved
by backpropagation are successful when solving
real-world tasks.
Adaptive Learning of Polynomial Networks is a vital reference for researchers and practitioners in the fields of evolutionary computation, artificial neural networks and Bayesian inference, and for advanced-level students of genetic programming. Readers will strengthen their skills in creating efficient model representations and learning operators that efficiently sample the search space, and in navigating the search process through the design of objective fitness functions.",
- size = "XIV, 316 pages",
