Overview
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 \xa0Kernel methods for structured data \xa0Multiple classifier systems \xa0Self organisation and modal learning \xa0Applications to content-based image retrieval, text mining in large document collections, and bioinformatics \xa0This book is thought particularly for graduate students, researchers and practitioners, willing to deepen their knowledge on more advanced connectionist models and related learning paradigms.
