A Novel Model of Working Set Selection for SMO Decomposition Methods
Machine Learning
2016-11-15 v1 Artificial Intelligence
Abstract
In the process of training Support Vector Machines (SVMs) by decomposition methods, working set selection is an important technique, and some exciting schemes were employed into this field. To improve working set selection, we propose a new model for working set selection in sequential minimal optimization (SMO) decomposition methods. In this model, it selects B as working set without reselection. Some properties are given by simple proof, and experiments demonstrate that the proposed method is in general faster than existing methods.
Cite
@article{arxiv.0706.0585,
title = {A Novel Model of Working Set Selection for SMO Decomposition Methods},
author = {Zhendong Zhao and Lei Yuan and Yuxuan Wang and Forrest Sheng Bao and Shunyi Zhang Yanfei Sun},
journal= {arXiv preprint arXiv:0706.0585},
year = {2016}
}