Title :
Support Vector Regression Estimation Based on Non-uniform Lost Function
Author :
Song, Xiaofeng ; Zhou, Tong ; Zhang, Huanping
Abstract :
The performances of support vector regression estimation were analyzed. It was found that the insensitive factor epsiv can affect the performance of support vector regression estimation significantly. The noise inside the sample data should be considered in determining the insensitive factor epsiv when support vector regression was employed. A novel support vector regression based on non-uniform lost function (NLF-SVR) was proposed to deal with different noise data density function in different region. The formulation and algorithms of computing NLF-SVR were given. The test example showed that the outcomes of NLF-SVR are better than that of conventional SVR. NLF-SVR can be applied in physiological systems modeling
Keywords :
biology computing; estimation theory; noise; physiological models; regression analysis; support vector machines; noise data density function; nonuniform lost function; physiological systems modeling; support vector regression estimation; Biomedical engineering; Density functional theory; Machine learning; Machine learning algorithms; Modeling; Neural networks; Permission; Statistics; Support vector machines; Testing; non-uniform lost function; regression estimator; support vector machine;
Conference_Titel :
Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the
Conference_Location :
Shanghai
Print_ISBN :
0-7803-8741-4
DOI :
10.1109/IEMBS.2005.1616619
