WANG Qing-yun, HUANG Dao. Fixed Size Least Squares Support Vector Machines[J]. Journal of East China University of Science and Technology, 2006, (7): 772-775.
Citation:
WANG Qing-yun, HUANG Dao. Fixed Size Least Squares Support Vector Machines[J]. Journal of East China University of Science and Technology, 2006, (7): 772-775.
WANG Qing-yun, HUANG Dao. Fixed Size Least Squares Support Vector Machines[J]. Journal of East China University of Science and Technology, 2006, (7): 772-775.
Citation:
WANG Qing-yun, HUANG Dao. Fixed Size Least Squares Support Vector Machines[J]. Journal of East China University of Science and Technology, 2006, (7): 772-775.
For the defect of sparseness in regression predicting with least squares support vector(machines),fixed size LS_-SVM is adopted,which evades the problem of sparseness in LS_-SVM and takes on fast training speed.The simulation results indicate that fixed size LS_-SVM shortens the training time enormously and possesses good predicting precision on different datasets.
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