Title :
Smoothing Support Vector Machines for e-Insensitive Regressi
Author :
Xiong, Jinzhi ; Hu, Tianming ; Hu, Jinlian ; Li, Guangming ; Peng, Hong
Author_Institution :
Software Coll., Dongguan Univ. of Technol.
Abstract :
Researching smooth support vector machine (SVM) for regression is an active field in data mining. Recently, Lee et al. proposed the smooth SVM for insensitive regression, where smoothing functions play a vital role in smooth SVMs. This paper presents a comparative study on three smooth SVMs: smooth SVM, polynomial smooth SVM and smooth support vector regression. It also discusses promising directions of support vector regression for future work
Keywords :
data mining; regression analysis; smoothing methods; support vector machines; data mining; insensitive regression; polynomial smooth SVM; smooth support vector regression; smoothing function; support vector machines; Data mining; Educational institutions; Face detection; Face recognition; Handwriting recognition; Polynomials; Smoothing methods; Statistical learning; Support vector machine classification; Support vector machines;
Conference_Titel :
Intelligent Systems Design and Applications, 2006. ISDA '06. Sixth International Conference on
Conference_Location :
Jinan
Print_ISBN :
0-7695-2528-8
DOI :
10.1109/ISDA.2006.244
