Handbook on Neural Information Processing

Monica Bianchini

ISBN 10: 3642429890 / ISBN 13: 9783642429897

Verlag: Springer Berlin Heidelberg Mai 2015, 2015

Beschreibung

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. Bestandsnummer des Verkäufers 9783642429897

Bibliografische Details

Titel: Handbook on Neural Information Processing

Verlag: Springer Berlin Heidelberg Mai 2015

Erscheinungsdatum: 2015

Einband: Taschenbuch

Zustand: Neu

Über diesen Titel

Reseña del editor:

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.