Handbook on Neural Information Processing

ISBN 10: 3642366562 / ISBN 13: 9783642366567

Published by Springer, 2013

About this Item

pp. 560. Seller Inventory # 2648035249

Bibliographic Details

Title: Handbook on Neural Information Processing

Publisher: Springer

Publication Date: 2013

Binding: Hardcover

Condition: New

About this title

Synopsis:

HardCover. Pub Date :2014-02-28 Pages: 499 Language: English Publisher: Springer Handbook on Neural Information Processing (Intelligent Systems Reference Library) 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. Handbook on Neural Information Processing (Intelligent Systems Reference Library) is thought particularly for graduate students. researc...

From the Back Cover:

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.