£113.05

Springer Adaptive Learning of Polynomial Networks Book

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Description

Expand your knowledge of machine learning with Adaptive Learning of Polynomial Networks. Published by Springer, this text provides both theoretical and practical insights for developing algorithms that infer linear and non-linear multivariate models. It offers a structured methodology for the inductive learning of polynomial neural network models (PNN) directly from data. This book focuses on an organized identification process designed to help you discover models that provide strong generalization and prediction capabilities. Readers will see how PNN models evolved through genetic programming and refined via backpropagation can effectively solve real-world tasks. It serves as a primary reference for researchers and practitioners working in artificial intelligence and machine learning who need to implement advanced modeling techniques.

Key Features

Learn to develop algorithms for inferring both linear and non-linear multivariate models from complex datasets.

Master a specific methodology for the inductive learning of polynomial neural network models (PNN).

Follow an organized identification process to discover models that generalize and predict with high accuracy.

Explore how genetic programming and backpropagation work together to improve PNN model performance.

Gain practical knowledge for applying these evolutionary and Bayesian methods to solve real-world tasks.

Access an essential reference designed specifically for researchers and practitioners in the AI field.

Product Specifications

Format
Paperback
Domain
Amazon UK
Release Date
11 February 2011
Listed Since
14 June 2010

Barcode

No barcode data available

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