关键词: 支持向量机, 认知规律, 相对变换, 认知几何

Abstract:

School of Computer Science and Engineering, South China University of Technology, Guangzhou 510640, Guangdong, ChinaAbstract:Although a support vector machine （SVM） has excellent classification ability for small data sets, it is still inefficient for noisy or sparse data sets. As humans can effectively deal with noisy and sparse data, a geometric approach of modeling human＇s cognitive abilities is proposed in this paper. Moreover, a geometric model of the relative cognitive law is established via relative transformation and is then used to improve SVM. It is indicated from the simulation that the classification capability of the improved SVM for noisy and sparse data sets significantly increases.

Key words: support vector machine, cognitive law, relative transformation, cognitive geometry