Isbn: 9780387312392 - adaptive learning of polynomial networks: genetic programming, backpropagation and bayesian methods (17 risultati)

Hardcover/Pappeinband. Condizione: Sehr gut. 330 p. Very good. Shrink wrapped. / Sehr guter Zustand. In Folie verschweißt. Sprache: Englisch Gewicht in Gramm: 798.

Condizione: good. A copy that has been read, remains in good condition. All pages are intact, and the cover is intact. The spine and cover show signs of wear. Pages can include notes and highlighting and show signs of wear, and the copy can include "From the library of" labels or previous owner inscriptions. 100% GUARANTEE! Shipped with delivery confirmation, if you're not satisfied with purchase please return item! Ships via media mail.…

Condizione: Good. Your purchase helps support Sri Lankan Children's Charity 'The Rainbow Centre'. Ex-library, so some stamps and wear, but in good overall condition. Our donations to The Rainbow Centre have helped provide an education and a safe haven to hundreds of children who live in appalling conditions.…

Condizione: New.

Condizione: New.

Condizione: New. In English.

Condizione: As New. Unread book in perfect condition.

Hardcover. Condizione: Like New. Like New. book.

Condizione: As New. Unread book in perfect condition.

Buch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book provides theoretical and practical knowledge for develop ment of algorithms that infer linear and nonlinear models. It offers a methodology for inductive learning of polynomial neural network mod els from data. The design of such tools contributes to better statistical data modelling when addressing tasks from various areas like system identification, chaotic time-series prediction, financial forecasting and data mining. The main claim is that the model identification process involves several equally important steps: finding the model structure, estimating the model weight parameters, and tuning these weights with respect to the adopted assumptions about the underlying data distrib ution. When the learning process is organized according to these steps, performed together one after the other or separately, one may expect to discover models that generalize well (that is, predict well). The book off'ers statisticians a shift in focus from the standard f- ear models toward highly nonlinear models that can be found by con temporary learning approaches. Speciafists in statistical learning will read about alternative probabilistic search algorithms that discover the model architecture, and neural network training techniques that identify accurate polynomial weights. They wfil be pleased to find out that the discovered models can be easily interpreted, and these models assume statistical diagnosis by standard statistical means. Covering the three fields of: evolutionary computation, neural net works and Bayesian inference, orients the book to a large audience of researchers and practitioners.…

Condizione: gut. 2006. Adaptive Learning of Polynomial Networks. Genetic Programming, Backpropagation and Bayesian Methods. [Genetic and Evolutionary Computation]. In englischer Sprache. pages.

Condizione: New. pp. 336.

Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 model identification process by which to discover models that generalize and predict well. The book further facilitates the discovery of polynomial models for time-series prediction. 332 pp. Englisch.…

Gebunden. Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Offers a shift in focus from the standard linear models toward highly nonlinear models that can be inferred by contemporary learning approachesPresents alternative probabilistic search algorithms that discover the model architecture and neural net.…

Buch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Libri GmbH, Europaallee 1, 36244 Bad Hersfeld 332 pp. Englisch.…

Condizione: New. Print on Demand pp. 336 62 Illus.

Condizione: New. PRINT ON DEMAND pp. 336.