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Handbook on Neural Information Processing

  • Monica Bianchini
  • Marco Maggini
  • Lakhmi C. Jain

Part of the Intelligent Systems Reference Library book series (ISRL, volume 49)

Table of contents

  1. Front Matter
    Pages 1-18
  2. Yoshua Bengio, Aaron Courville
    Pages 1-28
  3. Sajid A. Marhon, Christopher J. F. Cameron, Stefan C. Kremer
    Pages 29-65
  4. Monica Bianchini, Marco Maggini
    Pages 67-96
  5. Liviu Goraş, Ion Vornicu, Paul Ungureanu
    Pages 97-141
  6. Paul C. Kainen, Věra Kůrková, Marcello Sanguineti
    Pages 143-181
  7. Paul C. Kainen, Andrew Vogt
    Pages 183-214
  8. Mohamed Farouk Abdel Hady, Friedhelm Schwenker
    Pages 215-239
  9. Hendrik Blockeel
    Pages 241-281
  10. Andrea Passerini
    Pages 283-333
  11. Francesco Gargiulo, Claudio Mazzariello, Carlo Sansone
    Pages 335-378
  12. Dominic Palmer-Brown, Chrisina Jayne
    Pages 379-400
  13. Wim Wiegerinck, Willem Burgers, Bert Kappen
    Pages 401-431
  14. Jing Li, Nigel M. Allinson
    Pages 433-469
  15. Ah Chung Tsoi, Markus Hagenbuchner, Milly Kc, ShuJia Zhang
    Pages 471-503
  16. Masood Zamani, Stefan C. Kremer
    Pages 505-525
  17. Back Matter
    Pages 527-537

About this book

Introduction

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.

Keywords

Computational Intelligence Neural Information Processing

Editors and affiliations

  • Monica Bianchini
    • 1
  • Marco Maggini
    • 2
  • Lakhmi C. Jain
    • 3
  1. 1., Dipto. Ingegneria dell'InformazioneUniversità degli Studi di SienaSienaItaly
  2. 2.Fac. Ingegneria, Dipto. Ingegneria dell'InformazioneUniversità SienaSienaItaly
  3. 3.University of Canberra, School of Electrical and InformationAdjunct ProfessorACTAustralia

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-36657-4
  • Copyright Information Springer-Verlag Berlin Heidelberg 2013
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-642-36656-7
  • Online ISBN 978-3-642-36657-4
  • Series Print ISSN 1868-4394
  • Series Online ISSN 1868-4408
  • Buy this book on publisher's site