| Summary: | 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.
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