基于KNN和RVM的分类方法——KNN RVM分类器

针对相关向量机(RVM)算法分类精度低、核参数选择困难等问题，文中提出临界滑动阈值的概念并以其为基础将RVM与K近邻(KNN)算法结合构建分类器——KNN-RVM分类器。从理论上提出并证明KNN-RVM分类过程等价于带软间隔约束的支持向量机的分类过程、KNN-RVM分类器等价于每类只选一个代表点的1-NN分类器、KNN-RVM分类效果优于RVM这3个结论。对这3个不同数据集进行实验证明临界滑动阈值的临界性与滑动性及KNN-RVM分类器的准确性、适应性及全局最优性，提高分类精度，减轻算法对核参数的依赖性，进而证明KNN-RVM分类器是一种有效的分类器。

KNN and RVM Based Classification Method: KNN-RVM Classifier

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.