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Unformatted text preview: 2 E = sum sum h(bmu(i),j) ||m(j) - x(i)|| i j where m(i) is the ith prototype vector of SOM, x(j) is the jth data vector, and h(.,.) is the neighborhood function. In case of fixed neighborhood and discreet data, the distortion measure can be interpreted as the energy function of the SOM. Note, though, that the learning rule that follows from the distortion measure is different from the SOM training rule, so SOM only minimizes the distortion measure approximately. If the 'prob' argument is given, the distortion measure can be interpreted as an expected quantization error when the neighborhood function values give the likelyhoods of accidentally assigning vector j to unit i. The normal quantization error is a special case of this with zero incorrect assignement likelihood. NOTE: when calculating BMUs and distances, the mask of the given map is used. See also SOM_QUALITY, SOM_BMUS, SOM_HITS. [ SOM Toolbox online doc ]...
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This note was uploaded on 05/23/2010 for the course CS 245 taught by Professor Dunno during the Spring '10 term at Aarhus Universitet.
- Spring '10
- Machine Learning