Innovations in neural information paradigms and applications
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Bibliographic Information
Innovations in neural information paradigms and applications
（Studies in computational intelligence, v. 247）
Springer, c2009
Available at 1 libraries
Description and Table of Contents
Description
Tremendous advances in all disciplines including engineering, science, health care, business, avionics, management, and so on, can also be attributed to the development of artificial intelligence paradigms. In fact, researchers are always interested in desi- ing machines which can mimic the human behaviour in a limited way. Therefore, the study of neural information processing paradigms have generated great interest among researchers, in that machine learning, borrowing features from human intelligence and applying them as algorithms in a computer friendly way, involves not only Mathem- ics and Computer Science but also Biology, Psychology, Cognition and Philosophy (among many other disciplines). Generally speaking, computers are fundamentally well-suited for performing au- matic computations, based on fixed, programmed rules, i.e. in facing efficiently and reliably monotonous tasks, often extremely time-consuming from a human point of view. Nevertheless, unlike humans, computers have troubles in understanding specific situations, and adapting to new working environments. Artificial intelligence and, in particular, machine learning techniques aim at improving computers behaviour in tackling such complex tasks. On the other hand, humans have an interesting approach to problem-solving, based on abstract thought, high-level deliberative reasoning and pattern recognition. Artificial intelligence can help us understanding this process by recreating it, then potentially enabling us to enhance it beyond our current capabilities.
Table of Contents
Advances in Neural Information Processing Paradigms.- Self-Organizing Maps for Structured Domains: Theory, Models, and Learning of Kernels.- Unsupervised and Supervised Learning of Graph Domains.- Neural Grammar Networks.- Estimates of Model Complexity in Neural-Network Learning.- Regularization and Suboptimal Solutions in Learning from Data.- Probabilistic Interpretation of Neural Networks for the Classification of Vectors, Sequences and Graphs.- Metric Learning for Prototype-Based Classification.- Bayesian Linear Combination of Neural Networks.- Credit Card Transactions, Fraud Detection, and Machine Learning: Modelling Time with LSTM Recurrent Neural Networks.- Towards Computational Modelling of Neural Multimodal Integration Based on the Superior Colliculus Concept.
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