DocumentCode
553095
Title
A new regression method based on SVM classification
Author
Dong Yi ; Cheng Wei ; Li Shengfeng
Author_Institution
Inst. of Appl. Math., Bengbu Coll., Bengbu, China
Volume
2
fYear
2011
fDate
26-28 July 2011
Firstpage
770
Lastpage
773
Abstract
SVM (Support Vector Machines) is a novel algorithm of machine learning which is based on SLT (Statistical Learning Theory). It can solve the problem characterized by nonlinear, high dimension, small sample and local minimizing perfectly. For non-linear problem, the forecasting technique of FCTR (First classification, then regression) was proposed, based on the classification approach of SVM and has carried on the simulation experiment. The experiment shows that the fitting value which obtains using the return to first would be more precise than directly. Using this method to food production forecast, its accuracy is superior to other production forecasting methods.
Keywords
food processing industry; learning (artificial intelligence); pattern classification; production engineering computing; regression analysis; support vector machines; FCTR forecasting technique; SVM classification; first classification then regression; food production forecast; machine learning; regression method; statistical learning theory; support vector machines; Educational institutions; Forecasting; Integrated circuit modeling; Numerical models; Predictive models; Production; Support vector machines; first classification, then regression (FCTR); forecast; support vector machines (SVM);
fLanguage
English
Publisher
ieee
Conference_Titel
Fuzzy Systems and Knowledge Discovery (FSKD), 2011 Eighth International Conference on
Conference_Location
Shanghai
Print_ISBN
978-1-61284-180-9
Type
conf
DOI
10.1109/FSKD.2011.6019663
Filename
6019663
Link To Document