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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
• ...

Presenting the fundamentals and practical applications of neural information systems, this volume presents the recent developments in the field. Self-organizing structures, neural grammar networks and model complexity are among the many topics discussed.