Adaptive Learning of Polynomial Networks : Genetic Programming, Backpropagation and Bayesian Methods
- Author
- Nikolay Y. Nikolaev, Hitoshi Iba
- Publication Year
- 2006
- Publisher
- Springer
- Language
- English
- Document Type
- Book
- Faculty / Subject Heading
- Computer Science
- Download Book Read book
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.
Keywords: Computer science / Bayesian inference/ algorithms / Artificial intelligence / Genetic programming / Intelligence / Learning / Machine learning / Navigation / Programming
