Further, we expect that the recognition rate to improve because of the additional re training required by our methodology. In this paper we will draw examples from handwritten word recognition to illustrate our point. The transformed values are treated as features and input to a neural network functioning as another classifier. They identify desirable properties of combination including omnitype, mixed type, correlated classifier combination, redundant classifier elimination, model complexity control, and dynamic selection. A classifier can output a unique class decision, ranked candidate classes, or candidate classes with associated measurement values.
Most combination approaches combine decisions of a single type.
We also observe that most approaches organize classifiers in a parallel fashion rather than a serial fashion. In this case, the function which the combination system has to approximate is usually complex and highly nonlinear. Therefore , the utilization of neural networks for the combination seems promising. The remainder of the paper is organized as follows.