Handbook on Neural Information Processing [Paperback]

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

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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.

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 learniló-