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A comprehensive guide to inductive learning of polynomial neural network (PNN) models from data, combining evolutionary search (genetic programming) for structure discovery, specialized backpropagation for weight tuning, and Bayesian inference for generalization. The book develops a complete methodology for constructing nonlinear, interpretable polynomial networks and demonstrates their use for tasks such as time‑series prediction, system identification, and data mining.

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245: $a: Adaptive Learning of Polynomial Networks: Genetic Programming, Backpropagation and Bayesian Methods
245: $b:
082: $a:
100: $a: Nikolay Y. Nikolaev & Hitoshi Iba
650: $a: Computer Science
250: $a:
440: $v:
250: $b: Springer
250: $c: 2006
020: $a: 978-0387312392
300: $a: 316