Title of article :
Online independent reduced least squares support vector regression
Author/Authors :
Yongping Zhao، نويسنده , , Jianguo Sun، نويسنده , , Zhong-Hua Du، نويسنده , , Zhi-An Zhang، نويسنده , , Yebo Li، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2012
Abstract :
In this paper, an online algorithm, viz. online independent reduced least squares support vector regression (OIRLSSVR), is proposed based on the linear independence and the reduced technique. As opposed to some offline algorithms, OIRLSSVR takes the realtime advantage, which is confirmed using benchmark data sets. In comparison with online algorithm, the realtime of OIRLSSVR is also favorable. As for this point, it is tested with experiments on the benchmark data sets and a more realistic scenario namely a diesel engine example. All in all, OIRLSSVR can enhance the modeling realtime, especially for the case where the samples enter in a flow mode.
Keywords :
Support vector regression , Machine Learning , Online algorithm , Offline algorithm , Linear independence
Journal title :
Information Sciences
Journal title :
Information Sciences