Handbook on Neural Information Processing: Intelligent Systems Reference Library, cartea 49
Editat de Monica Bianchini, Marco Maggini, Lakhmi C. Jainen Limba Engleză Paperback – 22 mai 2015
- 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.
Toate formatele și edițiile | Preț | Express |
---|---|---|
Paperback (1) | 947.14 lei 6-8 săpt. | |
Springer Berlin, Heidelberg – 22 mai 2015 | 947.14 lei 6-8 săpt. | |
Hardback (1) | 953.22 lei 6-8 săpt. | |
Springer Berlin, Heidelberg – 26 apr 2013 | 953.22 lei 6-8 săpt. |
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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
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.
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.
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