Handbook on Neural Information Processing

Monica Bianchini

ISBN 10: 3642429890 / ISBN 13: 9783642429897

Published by Springer Berlin Heidelberg Mai 2015, 2015

About this Item

This item is printed on demand - it takes 3-4 days longer - Neuware -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. 560 pp. Englisch.

Seller Inventory # 9783642429897

Bibliographic Details

• Title: Handbook on Neural Information Processing
• Publisher: Springer Berlin Heidelberg Mai 2015
• Publication Date: 2015
• Language: English
• ISBN 10: 3642429890
• ISBN 13: 9783642429897
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 30D millimeters 235 millimeters width by 155 millimeters height by 30 millimeters depth
• Weight: 838 grams

About this title

Synopsis

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.