A Pattern Selection Algorithm Based on the Generalized Confidence

In the process of training, some patterns are indispensable because they describe the characteristic of the class, but other patterns are dispensable. Sometimes, with these patterns the system performance even gets worse. So it is necessary to select the training patterns and find a more representative pattern subset. In this paper, a definition of the boundary patterns based on the generalized confidence is given, and a new algorithm of pattern selection is founded on this definition. According to the experiments on the offline handwritten Chinese character database HCL2004, the pattern subset selected by these algorithms have less patterns than the original set, but the system performance based on the subset is improved. Then the validity of the definition and these algorithms is approved