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Handbook on Neural Information Processing: Intelligent Systems Reference Library, cartea 49

Editat de Monica Bianchini, Marco Maggini, Lakhmi C. Jain
en Limba Engleză Paperback – 22 mai 2015
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.
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Specificații

ISBN-13: 9783642429897
ISBN-10: 3642429890
Pagini: 560
Ilustrații: XX, 538 p.
Dimensiuni: 155 x 235 x 29 mm
Greutate: 0.78 kg
Ediția:2013
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seria Intelligent Systems Reference Library

Locul publicării:Berlin, Heidelberg, Germany

Cuprins

Neural Network Architectures.- Learning paradigms.-
Reasoning and applications.- conclusions.
Reasoning and applications.- conclusions.
Reasoning and applications.- conclusions.

Textul de pe ultima copertă

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.

Caracteristici

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