An Efficient Classifier Based on Hierarchical Mixing Linear Support Vector Machines / 3897
Di Wang, Xiaoqin Zhang, Mingyu Fan, Xiuzi Ye
Support vector machines (SVMs) play a very dominant role in data classification due to their good generalization performance. However, they suffer from the high computational complexity in the classification phase when there are a considerable number of support vectors (SVs). Then it is desirable to design efficient algorithms in the classification phase to deal with the datasets of real-time pattern recognition systems. To this end, we propose a novel classifier called HMLSVMs (Hierarchical Mixing Linear Support Vector Machines) in this paper, which has a hierarchical structure with a mixing linear SVMs classifier at each node and predicts the label of a sample using only a few hyperplanes. We also give a generalization error bound for the class of locally linear SVMs (LLSVMs) based on the Rademacher theory, which ensures that overfitting can be effectively avoided. Experimental evaluations shows, while maintaining a comparable classification performance to kernel SVMs (KSVMs), the proposed classifier achieves the high efficiency in the classification stage.