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.[1][2] 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-organizing maps (Kohonen maps).
- ^ Rumelhart, David; David Zipser; James L. McClelland; et al. (1986). Parallel Distributed Processing, Vol. 1. MIT Press. pp. 151–193.
- ^ Grossberg, Stephen (1987-01-01). "Competitive learning: From interactive activation to adaptive resonance" (PDF). Cognitive Science. 11 (1): 23–63. doi:10.1016/S0364-0213(87)80025-3. ISSN 0364-0213.
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