Intelligent Systems Reference Library
© 2013
Handbook on Neural Information Processing
Editors: Bianchini, Monica, Maggini, Marco, Jain, Lakhmi C. (Eds.)
Free Preview- Contains the latest research in the area of neural information systems and their applications
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- About this book
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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.
- Table of contents (15 chapters)
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Deep Learning of Representations
Pages 1-28
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Recurrent Neural Networks
Pages 29-65
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Supervised Neural Network Models for Processing Graphs
Pages 67-96
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Topics on Cellular Neural Networks
Pages 97-141
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Approximating Multivariable Functions by Feedforward Neural Nets
Pages 143-181
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Table of contents (15 chapters)
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Bibliographic Information
- Bibliographic Information
-
- Book Title
- Handbook on Neural Information Processing
- Editors
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- Monica Bianchini
- Marco Maggini
- Lakhmi C. Jain
- Series Title
- Intelligent Systems Reference Library
- Series Volume
- 49
- Copyright
- 2013
- Publisher
- Springer-Verlag Berlin Heidelberg
- Copyright Holder
- Springer-Verlag Berlin Heidelberg
- eBook ISBN
- 978-3-642-36657-4
- DOI
- 10.1007/978-3-642-36657-4
- Hardcover ISBN
- 978-3-642-36656-7
- Softcover ISBN
- 978-3-642-42989-7
- Series ISSN
- 1868-4394
- Edition Number
- 1
- Number of Pages
- XX, 538
- Topics
