EXPANDING THE PERFORMANCE OF POLYNOMIAL CLASSIFIERS BY ITERATIVE LEARNING

In this paper it is shown, how an existing polynomial classifier could be improved by iterative (reinforced) learning. In some experiments the effects of this algorithm are evaluated. Additionally it is shown how the learning factor is related to the length of the polynomial to gain a good convergence to reduce the error rate. Furthermore at the first time a complete quadratic polynomial classifier in 256 features resulting in a polynomial length of 33152 could be trained and evaluated with this algorithm.