Title :
Material erosion rate model based on PCLS-SVM
Author :
Liu, De-yong ; Fu, Dong-mei ; Zhang, Biao
Author_Institution :
Univ. of Sci. & Technol., Beijing
Abstract :
Support vector machine (SVM) is a novel machine learning method based on statistical learning theory. A material erosion rate model based on principal component least square SVM (PCLS-SVM) is proposed. PCA calculates principal components in high dimensional feature space and reduces dimensions of sample. Cross validation method is used to select parameters of PCLS-SVM model. PCLS-SVM is applied to prediction of material erosion rate. Results indicate that this method features high learning speed and well generalization ability.
Keywords :
least squares approximations; mechanical engineering computing; principal component analysis; support vector machines; wear; PCLS-SVM; cross validation method; machine learning method; material erosion rate model; principal component least square method; support vector machine; Data analysis; Data mining; Learning systems; Least squares methods; Machine learning; Materials science and technology; Principal component analysis; Space technology; Statistical learning; Support vector machines; learning theory; least square; principal component analysis; support vector machine; well generalization ability.;
Conference_Titel :
Wavelet Analysis and Pattern Recognition, 2007. ICWAPR '07. International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-1065-1
Electronic_ISBN :
978-1-4244-1066-8
DOI :
10.1109/ICWAPR.2007.4421652
