This handbook presents some of the most recent topics in neural information processing, covering both theoretical concepts and practical applications. The contributions include:
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.
"synopsis" may belong to another edition of this title.
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.
"About this title" may belong to another edition of this title.
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Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 560 pp. Englisch. Seller Inventory # 9783642366567
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Contains the latest research in the area of neural information systems and their applicationsWritten by leading expertsState-of-the-Art of the bookThis handbook presents some of the most recent topics in neural information proces. Seller Inventory # 5058355
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Buch. Condition: Neu. Handbook on Neural Information Processing | Monica Bianchini (u. a.) | Buch | xx | Englisch | 2013 | Springer | EAN 9783642366567 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand. Seller Inventory # 105977628
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Buch. Condition: Neu. Neuware -This handbook presents some of the most recent topics in neural information processing, covering both theoretical concepts and practical applications. The contributions include: Deep architecturesRecurrent, recursive, and graph neural networksCellular neural networksBayesian networksApproximation capabilities of neural networksSemi-supervised learningStatistical relational learningKernel methods for structured dataMultiple classifier systemsSelf organisation and modal learningApplications to content-based image retrieval, text mining in large document collections, and bioinformaticsThis book is thought particularly for graduate students, researchers and practitioners, willing to deepen their knowledge on more advanced connectionist models and related learning paradigms.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 560 pp. Englisch. Seller Inventory # 9783642366567
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Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Seller Inventory # 9783642366567
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