Hierarchical mixing linear support vector machines for nonlinear classification

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Di Wang, Xiaoqin Zhang, Mingyu Fan, Xiuzi Ye

Published: 2016, Last Modified: 07 Nov 2025Pattern Recognit. 2016EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: Highlights•H-MLSVMs does not require a large memory to store kernel values in the training process, because H-MLSVMs is composed of LSVMs, and there are very efficient algorithms for training LSVMs [48], [49].•The hierarchical structure makes H-MLSVMs predict the labels of new arrived samples via a few of LSVMs, and it is much faster than nonlinear SVMs classifiers.•We quantify the generalization error bound for the class of LLSVMs based on the Rademacher complexity, and the stop criterion based on minimizing this bound ensures that H-MLSVMs can effectively avoid overfitting and have a good classification performance.