Neural Network Data Analysis Using Simulnet™

Nonfiction, Computers, Advanced Computing, Artificial Intelligence, Database Management, General Computing
Cover of the book Neural Network Data Analysis Using Simulnet™ by Edward J. Rzempoluck, Springer New York
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Author: Edward J. Rzempoluck ISBN: 9781461217466
Publisher: Springer New York Publication: December 6, 2012
Imprint: Springer Language: English
Author: Edward J. Rzempoluck
ISBN: 9781461217466
Publisher: Springer New York
Publication: December 6, 2012
Imprint: Springer
Language: English

This book and software package complements the traditional data analysis tools already widely available. It presents an introduction to the analysis of data using neural network functions such as multilayer feed-forward networks using error back propagation, genetic algorithm-neural network hybrids, generalised regression neural networks, learning quantizer networks, and self-organising feature maps. In an easy-to-use, Windows-based environment it offers a wide range of data analytic tools which are not usually found together: genetic algorithms, probabilistic networks, as well as a number of related techniques that support these. Readers are assumed to have a basic understanding of computers and elementary mathematics, allowing them to quickly conduct sophisticated hands-on analyses of data sets.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

This book and software package complements the traditional data analysis tools already widely available. It presents an introduction to the analysis of data using neural network functions such as multilayer feed-forward networks using error back propagation, genetic algorithm-neural network hybrids, generalised regression neural networks, learning quantizer networks, and self-organising feature maps. In an easy-to-use, Windows-based environment it offers a wide range of data analytic tools which are not usually found together: genetic algorithms, probabilistic networks, as well as a number of related techniques that support these. Readers are assumed to have a basic understanding of computers and elementary mathematics, allowing them to quickly conduct sophisticated hands-on analyses of data sets.

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