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Competitive learning is a form of unsupervised learning in artificial neural networks, in which nodes compete for the right to respond to a subset of the input data. A variant of Hebbian learning, competitive learning works by increasing the specialization of each node in the network. It is well suited to finding clusters within data. Models and algorithms based on the principle of competitive learning include vector quantization and self-organising maps (Kohonen maps). == Principles == There are three basic elements to a competitive learning rule:〔Rumelhart, David E., and David Zipser. "Feature discovery by competitive learning." Cognitive science 9.1 (1985): 75-112.〕〔Haykin, Simon, "Neural Network. A comprehensive foundation." Neural Networks 2.2004 (2004).〕 * A set of neurons that are all the same except for some randomly distributed synaptic weights, and which therefore respond differently to a given set of input patterns * A limit imposed on the "strength" of each neuron * A mechanism that permits the neurons to compete for the right to respond to a given subset of inputs, such that only one output neuron (or only one neuron per group), is active (i.e. "on") at a time. The neuron that wins the competition is called a "winner-take-all" neuron. Accordingly the individual neurons of the network learn to specialize on ensembles of similar patterns and in so doing become 'feature detectors' for different classes of input patterns. The fact that competitive networks recode sets of correlated inputs to one of a few output neurons essentially removes the redundancy in representation which is an essential part of processing in biological sensory systems.〔Barlow, Horace B. "Unsupervised learning." Neural computation 1.3 (1989): 295-311.〕〔Edmund T.. Rolls, and Gustavo Deco. Computational neuroscience of vision. Oxford: Oxford university press, 2002.〕 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「competitive learning」の詳細全文を読む スポンサード リンク
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