Handbook on Neural Information Processing

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.

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

Inhalt
Neural Network Architectures.- Learning paradigms.- Reasoning and applications.- conclusions. Reasoning and applications.- conclusions. Reasoning and applications.- conclusions.