分片支撑矢量机

Piecewise Support Vector Machines

文中借鉴了分段线性识别的基本思想,提出了分片支撑矢量机模型.该模型首先将特征空间剖分成若干子空间,在每个子空间中基于支撑矢量机构造一个最优分类面,然后,将各个分类面链接起来构成一个分片最优分类面以逼近理论上的最优分类超曲面.同时,文中还从理论上分析探讨了其推广能力的界,为分片支撑矢量机模型提供了坚实的基础.最后,经典双螺旋线数据实验结果表明,相对于传统支撑矢量机,分片支撑矢量机的计算速度、分类能力以及推广能力均有了明显提高.

A novel piecewise support vector machines （PSVM） model is provided in this paper; which used the traditional piecewise linear recognition method for reference. In this new model, the feature space was firstly partitioned into several subspaces, then the piecewise classification surface was developed by linking the optimal classification surfaces in each subspaces based on SVM. As the experimental results shown, validated with the Two Spiral Data and the actually measuring data, the performance of PSVM such as computational efficiency, classification, capa- bility and generalized capability are improved obviously in contrast to SVM.