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.