Explain competitive learning.

Competitive learning :-

• 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-organizing maps.

• In a competitive learning model, there are hierarchical sets of units in the network with inhibitory and excitatory connections.

• The excitatory connections are between individual layers and the inhibitory connections are between units in layered clusters.

• Units in a cluster are either active or inactive.

• There are three basic elements to a competitive learning rule :

  • 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.