Sequential training of Support Vector Machine

In this paper we present efficient implementation of an algorithm for sequential training of Support Vector Machine. Algorithm is obtained by maintaining Karush-Kuhn-Tucker optimality conditions while learning from new example. We have tested the performance of our implementation on widely recognized classification benchmark tests.