Unsupervised competitive learning

Unsupervised Competitive Learning This learning algorithm is used if no information about the class membership of the training data vectors is available. The change of the weights at iteration t is updated by... 

The aim of Kohonen learning is to map similar signals to similar neuron positions. The learning procedure is unsupervised competitive learning where in each cycle the neuron c is to be found with the output most similar to the input signal ... 

A subset of VQ algorithms comprises Competitive Learning (CL) methods, where a neural network model is used to find an approach of VQ calculation in an unsupervised way. Their advantage over other VQ algorithms is that CL is simple and easily parallelizable. Well known CL approaches are K-means [13] (including... 

Neural networks for unsupervised learning are based on a competitive layer of weights arranged linearly or in a plane (Figure 8.13). If arranged in a plane, the nets are termed a Kohonen network. The peculiarity of this network type is the maintenance of topology or, more general, the pattern of the data vector to be learned. 

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