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This paper surveys main principles of feature selection and their recent applications in big data bioinformatics. Instead of the commonly used categorization into filter, wrapper, and embedded approaches to feature selection, we formulate feature selection as a combinatorial optimization or search problem and categorize feature selection methods into exhaustive search, heuristic search, and hybrid...
Two major categories of unsupervised learning rules are used in artificial neural networks: (i) competitive learning, which is used in the adaptive resonance theory (ART), the self-organizing map, and the neocognitron; and (ii) Hebbian learning without lateral inhibition, which is used in the Hopfield network. Since the competitive learning is essentially Hebbian learning in the presence of lateral...
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