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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Competitivelearning is a form of unsupervised learning in artificial neural networks, in which nodes compete for the right to respond to a subset of...
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In machine learning, reinforcement learning from human feedback (RLHF) is a technique to align an intelligent agent to human preferences. In classical...
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strategic decision-making in the context of complex environments and competitive dynamics. Strategic management is not static in nature; the models can...
A learning curve is a graphical representation of the relationship between how proficient people are at a task and the amount of experience they have....
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