Margin-setting with hyperellipsoidal surfaces
Kaveh Heidary, H. John Caulfield · Optical Memory and Neural Networks · 2010
In a number of prior papers we described and explored a new pattern recognition method called Margin-Setting that accomplishes excellent generalization using very few training samples. The result was a multi-round classifier with each round consisting of a set of hyperspheres such that if a datum fell within a certain hypersphere, it was labeled with one particular class. Margin-Setting achieves concurrent low Vapnik-Chervonenkis (VC) dimension and high accuracy which is a consequence of partitioning the training set into smaller sets that make this possible. This paper extends Margin-Setting from hyperspheres to hyperellipsoids resulting in improved performance.