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 :
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