Overview
- Contains the latest research in the area of neural information systems and their applications
- Written by leading experts
- State-of-the-Art of the book
Part of the book series: Intelligent Systems Reference Library (ISRL, volume 49)
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Table of contents (15 chapters)
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Front Matter
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Back Matter
Keywords
About this book
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.
Editors and Affiliations
Bibliographic Information
Book Title: Handbook on Neural Information Processing
Editors: Monica Bianchini, Marco Maggini, Lakhmi C. Jain
Series Title: Intelligent Systems Reference Library
DOI: https://doi.org/10.1007/978-3-642-36657-4
Publisher: Springer Berlin, Heidelberg
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2013
Hardcover ISBN: 978-3-642-36656-7Published: 26 April 2013
Softcover ISBN: 978-3-642-42989-7Published: 22 May 2015
eBook ISBN: 978-3-642-36657-4Published: 12 April 2013
Series ISSN: 1868-4394
Series E-ISSN: 1868-4408
Edition Number: 1
Number of Pages: XX, 538