Handbook on Neural Information Processing (9783642366567)

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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.

Product details

• Format Hardback | 538 pages
• Dimensions 155 x 235 x 30.23mm | 9,575g
• Publication date 26 Apr 2013
• Publisher Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
• Imprint Springer-Verlag Berlin and Heidelberg GmbH & Co. K
• Publication City/Country Berlin, Germany
• Language English
• Edition Statement 2013 ed.
• Illustrations note XX, 538 p.
• ISBN10 3642366562
• ISBN13 9783642366567

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