Innovations in Neural Information Paradigms and Applications

Abstract

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.