Support vector machine (SVM) is one of the most powerful techniques for supervised classification. However, the performances of SVMs are based on choosing the proper kernel functions or proper parameters of a kernel function. It is extremely time consuming by applying the k-fold cross-validation (CV) to choose the almost best parameter. Nevertheless, the searching range and fineness of the grid method should be determined in advance. In this paper, an automatic method for selecting the parameter of the RBF kernel function is proposed. In the experimental results, it costs very little time than k-fold cross-validation for selecting the parameter by our proposed method. Moreover, the corresponding SVMs can obtain more accurate or at least equal performance than SVMs by applying k-fold cross-validation to determine the parameter.
|Name||IEEE International Symposium on Geoscience and Remote Sensing IGARSS|
|Conference||30th IEEE International Geoscience and Remote Sensing Symposium (IGARSS) on Remote Sensing - Global Vision for Local Action|
|Period||25/06/10 → 30/06/10|
- Support vector machine; kernel method; optimal kernel