Computer Engineering and Applications ›› 2008, Vol. 44 ›› Issue (5): 242-245.

• 工程与应用 • Previous Articles     Next Articles

Application of least squares support vector machines to OSAHS

ZHANG Xiao-dan,SHAO Shuai,LIU Qin-sheng   

  1. School of Applied Science,University of Science and Technology Beijing,Beijing 100083,China
  • Received:2007-06-13 Revised:2007-10-23 Online:2008-02-11 Published:2008-02-11
  • Contact: ZHANG Xiao-dan

最小二乘支持向量机在睡眠打鼾诊断中的应用

张晓丹,邵 帅,刘钦圣   

  1. 北京科技大学 应用科学学院 数学系,北京 100083

  • 通讯作者: 张晓丹

Abstract: Support Vector Machines(SVM) is one of the most important data mining and machine learning method,and Least Squares Support Vector Machines(LS-SVM) is extended from Support Vector Machines learning algorithm and better in learning speed.The LS-SVM is applied to the multi-class classification methods of SVM including 1-a-r,1-a-1,ECOC and MOC.Then,the multi-class classification methods of LS-SVM are applied to OSAHS diagnoses,which have a good result.

Key words: Least Squares Support Vector Machines, multi-class classification, Minimum Output Coding, Obstructive Sleep Apnea Hypopnea Syndrome

摘要: 支持向量机是数据挖掘和机器学习领域中的重要方法之一,最小二乘支持向量机是支持向量机学习算法的重要扩展,在训练速度方面有明显优势。对支持向量机现有的多类分类算法(一对一方法、一对多方法、纠错输出编码方法和最小输出编码方法)引入了最小二乘支持向量机,并应用于睡眠打鼾疾病的诊断预测中,取得了较好的效果。

关键词: 最小二乘支持向量机, 多类分类, 最小输出编码, 阻塞性睡眠呼吸暂停低通气综合征