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Handbook on Neural Information Processing [electronic resource] / edited by Monica Bianchini, Marco Maggini, Lakhmi C. Jain.

Contributor(s): Bianchini, Monica [editor.] | Maggini, Marco [editor.] | Jain, Lakhmi C [editor.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Intelligent Systems Reference Library: 49Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013Description: XX, 538 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783642366574.Subject(s): Engineering | Artificial intelligence | Computational intelligence | Engineering | Computational Intelligence | Artificial Intelligence (incl. Robotics)Additional physical formats: Printed edition:: No titleDDC classification: 006.3 Online resources: Click here to access online
Contents:
Neural Network Architectures -- Learning paradigms -- Reasoning and applications -- conclusions. Reasoning and applications -- conclusions. Reasoning and applications -- conclusions.
In: Springer eBooksSummary: 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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Neural Network Architectures -- Learning paradigms -- Reasoning and applications -- conclusions. Reasoning and applications -- conclusions. Reasoning and applications -- conclusions.

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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