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Handbook on Neural Information Processing

Name: Handbook on Neural Information Processing
SKU: 9783642429897
Price: 1592.1 NOK
Availability: SoldOut
ISBN: 9783642429897

Redigert av Lakhmi C. Jain, Monica Bianchini, Marco Maggini, 2015.Del av serien Intelligent Systems Reference Library.

This handbook presents some of the most recent topics in neural information processing, covering both theoretical concepts and practical applications. The contributions include:

Deep architectures
Recurrent, recursive, and graph neural networks
Cellular neural networks
Bayesian networks
Approximation capabilities of neural networks
Semi-supervised learning
Statistical relational learning
Kernel methods for structured data
Multiple classifier systems
Self organisation and modal learning
Applications to content-based image retrieval, text mining in large document collections, and bioinformatics

This book is thought particularly for graduate students, researchers and practitioners, willing to deepen their knowledge on more advanced connectionist models and related learning paradigms.

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Normalpris: 1 769,-

1 592,-

Spar 177,-

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Andre bøker i serien Intelligent Systems Reference Library

Handbook on Neural Information Processing

Redigert av Lakhmi C. Jain, Monica Bianchini, Marco Maggini, 2015.Del av serien Intelligent Systems Reference Library.

This handbook presents some of the most recent topics in neural information processing, covering both theoretical concepts and practical applications. The contributions include:

Deep architectures
Recurrent, recursive, and graph neural networks
Cellular neural networks
Bayesian networks
Approximation capabilities of neural networks
Semi-supervised learning
Statistical relational learning
Kernel methods for structured data
Multiple classifier systems
Self organisation and modal learning
Applications to content-based image retrieval, text mining in large document collections, and bioinformatics

This book is thought particularly for graduate students, researchers and practitioners, willing to deepen their knowledge on more advanced connectionist models and related learning paradigms.