%0 Book %1 nikolaev:2006:book %A Nikolaev, Nikolay %A Iba, Hitoshi %B Genetic and Evolutionary Computation %D 2006 %I Springer %K Bayesian adaptive algorithms, artificial backpropagation, computation, evolutionary genetic inference, intelligence, learning, networks, polynomial prediction programming, time-series %N 4 %T Adaptive Learning of Polynomial Networks Genetic Programming, Backpropagation and Bayesian Methods %X 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. %@ 0-387-31239-0

@book{nikolaev:2006:book, 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.}, added-at = {2008-06-19T17:46:40.000+0200}, author = {Nikolaev, Nikolay and Iba, Hitoshi}, biburl = {https://www.bibsonomy.org/bibtex/29bba65a708980f496e7962cf9fd9153c/brazovayeye}, interhash = {1242001f88cfcfed25f915645109b80c}, intrahash = {9bba65a708980f496e7962cf9fd9153c}, isbn = {0-387-31239-0}, keywords = {Bayesian adaptive algorithms, artificial backpropagation, computation, evolutionary genetic inference, intelligence, learning, networks, polynomial prediction programming, time-series}, note = {June}, number = 4, publisher = {Springer}, series = {Genetic and Evolutionary Computation}, size = {XIV, 316 pages}, timestamp = {2008-06-19T17:48:26.000+0200}, title = {Adaptive Learning of Polynomial Networks Genetic Programming, Backpropagation and Bayesian Methods}, year = 2006 }