Innovations in Neural Information Paradigms and Applications

Chapter 1 Innovations in Neural Information Paradigms and Applications

Chapter 2 Self-Organizing Maps for Structured Domains: Theory, Models, and Learning of Kernels

Chapter 3 Unsupervised and Supervised Learning of Graph Domains

Chapter 5 Estimates of Model Complexity in Neural-Network Learning

Chapter 6 Regularization and Suboptimal Solutions in Learning from Data

Chapter 7 Probabilistic Interpretation of Neural Networks for the Classification of Vectors, Sequences and Graphs

Chapter 8 Metric Learning for Prototype-Based Classification

Chapter 9 Bayesian Linear Combination of Neural Networks

Chapter 10 Credit Card Transactions, Fraud Detection, and Machine Learning: Modelling Time with LSTM Recurrent Neural Networks

Chapter 11 Towards Computational Modelling of Neural Multimodal Integration Based on the Superior Colliculus Concept