v-support vector classification with uncertainty based on expert advices

Support vector techniques have been successfully applied to many real-world problems, but it is difficult to select the parameter C. The v-support vector classification (v-SVC) has the advantage of a parameter v on controlling the number of support vectors. However, it is required that every input must be exactly assigned to one of these two classes without any uncertainty. A new v-SVM technique is proposed which is able to deal with training data with uncertainty based on expert advices. Firstly, the meaning of the uncertainty is defined. Based on this meaning of uncertainty, the algorithm has been derived. This technique extends the application horizon of v-SVM greatly. As an application, the problem about early warning of grain production is solved by our algorithm.