Classifying Algorithm Based on P-SVM
SMO(Sequence Minimum Optimization) algorithm has less complexity of time and space compared with traditional block algorithm and fixative sample collection algorithm, however, as the sample needed in real application field is very large, marking on samples is a waste work of time and power. This paper propose a method that can reduce the demanding quantity of samples by introducing the information on prior knowledge to the samples. The paper also gives the related algorithm named P-SMO. In the application of heribalist disease data, putting the P-SVM idea and P-SMO algorithm into the experiment, the result show a great improvement in classifying efficiency.