| 期刊全稱 | Adaptive Learning of Polynomial Networks | | 期刊簡稱 | Genetic Programming, | | 影響因子2023 | Nikolay Y. Nikolaev,Hitoshi Iba | | 視頻video | http://file.papertrans.cn/145/144682/144682.mp4 | | 發(fā)行地址 | Offers a shift in focus from the standard linear models toward highly nonlinear models that can be inferred by contemporary learning approaches.Presents alternative probabilistic search algorithms tha | | 學(xué)科分類 | Genetic and Evolutionary Computation | | 圖書封面 |  | | 影響因子 | 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 ar | | Pindex | Book 2006 |
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