An adaptive algorithm for modifying hyperellipsoidal decision surfaces

The learning vector quantization (LVQ) algorithm is a common method which allows a set of reference vectors for a distance classifier to adapt to a given training set. The authors have developed a similar learning algorithm, the LVQ using the Mahalanabis distance metric (LVQ-MM), which manipulates hyperellipsoidal cluster boundaries as opposed to reference vectors. Regions of the input feature space are first enclosed by ellipsoidal decision boundaries, and then these boundaries are iteratively modified to reduce classification error. Results obtained by classifying the Iris data set are provided