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This research book presents some of the most recent advances in neural information processing models including both theoretical concepts and practical applications. The contributions include: Advances in neural information processing paradigms - Self organizing structures - Unsupervised and supervised learning of graph domains - Neural grammar networks - Model complexity in neural network learning - Regularization and suboptimal solutions in neural learning - Neural networks for the classification of vectors, sequences and graphs - Metric learning for prototype-based classification - Ensembles of neural networks - Fraud detection using machine learning - Computational modeling of neural multimodal integration. This book is directed to the researchers, graduate students, professors and practitioner interested in recent advances in neural information processing paradigms and applications.
Springer-Verlag Berlin and Heidelberg GmbH & Co. K
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