An efficient kernel matrix evaluation measure
Pattern Recognition
| Hi-index | 0.01 |
Support vector machine is a new field of machine learning. Generalization accuracy and response time are two important criterions of support vector machine used in practical application. It is hoped that it will minimum the number of training dataset and support vectors, simplify the algorithm realization on the condition of keeping classification accuracy. Based on the above consideration, a reduction algorithm combined SVM with KNN algorithm is presented. The experiment results show that the algorithm can reduce the number of training dataset and support vectors on the condition of keeping the classification accuracy of the original training dataset.