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What are the salient features of Kohonen's self organizing learning algorithm.

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What are the salient features of Kohonen's self organizing learning algorithm.

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**Kohonen’s Self Organizing Map:**

- Kohonen self organizing maps refers also known as Kohonen feature maps or topology preserving maps are another competition based network pradigm for data clustering.
- Networks of this type impose a neighbourhood constraint on the output units, such that certain topological property in the input data is reflected in the output units weights
- The learning procedure of Kohonen feature maps is similar to that of competitive learning networks i.e. a similarity (dissimilarity) measure is selected and the winning unit is considered to be the one with the largest (smallest) activation.
- For Kohonen feature maps, we update not only the winning unit’s weights but also of the weights in a neighbourhood around the winning units.
- If the output units of a competitive learning network are arranged in a geometric manner (such as a 1D vector or 2D array) then we can update the weights of the winners as well as the neighbouring losers.

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