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 organising 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 modelling 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.
