Handbook on Neural Information Processing (Paperback, 2013 ed.)

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.

General

Imprint:	Springer-Verlag
Country of origin:	Germany
Series:	Intelligent Systems Reference Library, 49
Release date:	May 2015
Editors:	Monica Bianchini • Marco Maggini • Lakhmi C. Jain
Dimensions:	235 x 155 x 29mm (L x W x T)
Format:	Paperback
Pages:	538
Edition:	2013 ed.
ISBN-13:	978-3-642-42989-7
Categories:	Books > Computing & IT > Applications of computing > Artificial intelligence > General
LSN:	3-642-42989-0
Barcode:	9783642429897

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