earticle

영어

Support vector machine (SVM) has been widely applied to small-sample, non-linear and high-dimensional classifications. Many modified SVM algorithms were put forward in recent years. Some of them focus on SVM feature selection and some focus on SVM classification effectiveness. As different input vectors have significant influence on learning effect of decision boundary, this paper proposes a weighted multi-class support vector machine (WSVM) algorithm. The algorithm gives different weights to features according to the importance of their information. WSVM algorithm establishes decision boundaries based on weights and is used to classify educational resources. Experimental results indicate that the method achieves relatively good classification effectiveness.