Simplified polynomial network classifier for handwritten character recognition

Class-specific feature polynomial classifier (CFPC), a variant of a polynomial classifier (PC), yields high classification accuracy especially in high dimensional feature spaces. However, the computational cost for classification in such a high dimensional space is rather expensive. To overcome this difficulty, we propose a simplified polynomial network (SPN) classifier that reduces the complexity of polynomial networks with little deterioration of classification accuracy. In experiments of handwritten digit recognition on USPS, SPN using features of 30.0 dimensions on average achieved higher classification accuracy and a classification speed about 12.8 times faster than CFPC using features of 250 dimensions. In experiments on MNIST, SPN using features of 40.0 dimensions on average achieved a classification speed about 2.0 times faster than CFPC using features of 100 dimensions with nearly the same classification accuracy.