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A competitive network is typically a type of unsupervised machine learning, using the principle of competitive learning to provide results. Through specific mathematical and network modeling, competitive networks achieve various goals in input recognition and processing.
Competitive networks are also known as competitive neural networks.
Key to the design of competitive networks is the idea of excitatory and inhibitive influences on an artificial neuron. Experts talk about lateral inhibition and feedback connections through which laterally placed nodes may inhibit others. At the same time, feedforward excitatory synaptic connections can provide other corresponding influences. Understanding the ways that these work together are part of the road map toward working closely with competitive networks to drive new outcomes in machine learning and artificial intelligence.