Abstract Aimming at the problems of relevance vector machine (RVM) classification such as low precision and difficulty in kernel parameter selection, a concept called critical sliding threshold is presented in this paper. A classifier combining RVM with K nearest neighbour (KNN) called KNN-RVM classifier is constructed. In theory, three theorems is proposed and proved. The first is that the process of KNN-RVM classification is equivalent to an implementation of soft margin SVM. The second is that KNN-RVM classifier is equivalent to a 1NN classifier in which only one representative point is selected for each class. The last is the result of KNN-RVM classification is superior to that of RVM classification. The sliding and critical characteristics of critical sliding threshold are proved using three different datasets. The veracity, adaptability and global optimality of KNN-RVM classifier are proved as well. The KNN-RVM classifier improves the classification precision, reduces the reliance of algorithm on the kernel parameter, and thereby is proved to be an effective and excellent classifier.
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2010, Vol. 23 
