Handbook on neural information processing
著者
書誌事項
Handbook on neural information processing
（Intelligent systems reference library, v. 49）
Springer, c2013
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注記
Includes bibliographical references and index
内容説明・目次
内容説明
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.
目次
Neural Network Architectures.- Learning paradigms.-
Reasoning and applications.- conclusions.
Reasoning and applications.- conclusions.
Reasoning and applications.- conclusions.
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