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Handbook on Neural Information Processing

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        ...

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Bibliographic Details
Other Authors: Bianchini, Monica (Editor), Maggini, Marco (Editor), Jain, Lakhmi C. (Editor)
Format: eBook
Language:English
Published: Berlin, Heidelberg Springer Berlin Heidelberg 2013, 2013
Edition:1st ed. 2013
Series:Intelligent Systems Reference Library
Subjects:
Computational Intelligence
Artificial Intelligence
Online Access:
Collection: Springer eBooks 2005- - Collection details see MPG.ReNa
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520 |a 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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