Detailansicht

InhaltsangabeInductive Genetic Programming.- Tree-Like PNN Representations.- Fitness Functions and Landscapes.- Search Navigation.- Backpropagation Techniques.- Temporal Backpropagation.- Bayesian Inference Techniques.- Statistical Model Diagnostics.- Time Series Modelling.- Conclusions.

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 network training techniques to find accurate polynomial weightsFacilitates the discovery of polynomial models for time-series predictionIncludes supplementary material: sn.pub/extras