Research Article
Prototype Generation Using Self-Organizing Maps for Informativeness-Based Classifier
Algorithm 1
Prototype Generation based on SOM briefly described in a pseudo-code.
Input: The weight vectors of SOM Map trained (); training objects dataset (); and an unknown sample () | Output: the label of unknown sample () | Compare the unknown sample with each weight vector of using Eq. (3) | The units to be visited are defined by Eq. (4) and the input patterns objects are retrieved by Eq. (5), recovering a reduced dataset | training . | The reduced dataset and the unknown sample are used by a classifier (NN or NN) that return the object class. |
|