Title :
Output Prediction Model in Fully Mechanized Mining Face Based on Support Vector Machine
Author :
Li, Wanqing ; Yong Zhao ; Meng, Wenqing ; XU, Shipeng
Author_Institution :
Sch. of Econ. & Manage., HeBei Univ. of Eng. Handan, Handan
Abstract :
Support vector machine is a new machine learning technique developed on the basis of statistical learning theory, which has become the hotspot of machine learning because of its excellent learning performance. Based on analyzing the theory of support vector machine for regression (SVR), a SVR model is established for predicting the output in fully mechanized mining face, and then realizes the model by programming based on Matlab, finally, compared with genetic neural network prediction model. It shows that SVM has a higher accuracy of prediction than GNN, which proved the validity and practicality of the model.
Keywords :
data mining; learning (artificial intelligence); neural nets; prediction theory; regression analysis; support vector machines; Matlab; SVR model; fully mechanized mining face; genetic neural network prediction model; machine learning technique; output prediction model; statistical learning theory; support vector machine; support vector regression; Data engineering; Economic forecasting; Engineering management; Knowledge engineering; Knowledge management; Machine learning; Machine learning algorithms; Predictive models; Statistical learning; Support vector machines; Fully Mechanized Mining Face; Prediction; Support Vector Machine;
Conference_Titel :
Knowledge Discovery and Data Mining, 2009. WKDD 2009. Second International Workshop on
Conference_Location :
Moscow
Print_ISBN :
978-0-7695-3543-2
DOI :
10.1109/WKDD.2009.41
