Kohonen Networks are used to produce a low-dimensional representation of the input. KNs are trained in an unsupervised learning manner. Every node is chosen at random from the set of training data and every node is examined to calculate which one’s weights are most like the input vector. The winning node is called the Best Matching Unit (BMU). The neighbouring node’s weights are adjusted to make them like the input vector, hence finishing training. Self organizing models are used pervasively in Image Browsing Systems, Medical Diagnosis, and Environmental Modelling.
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