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In pattern recognition and machine learning, a feature vector is an n-dimensional vector of numerical features that represent some object.
over time, the learning rate decreases to let the feature vectors stabilize. the neighborhood size is the distance away from the BMU that Map Nodes will have their feature vectors adjusted.
Suppose that we have n sample feature vectors x1, x2, ..., xn all from the same class, and we know that they fall into k compact clusters, k < n. Let mi be the mean of the vectors in cluster i.
 See also: Classification, Neural network, Dimensionality, Clustering, Mapping
  
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