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Improved Scheme for Fast Approximation to Least Squares Support Vector Regression |
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| Authors: | Zhang Yuchen Zhao Yongping Song Chengjun Hou Kuanxin Tuo Jinkui Ye Xiaojun |
| Affiliation: | 1. School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing, 210094, P.R.China 2. Military Ammunition in Shenyang Representative Office, Shenyang, 110045, P.R.China 3. Civil Aviation Flight University of China, Guanghan, 618307, P.R.China 4. Heilongjiang North Tool Company Limited, Mudanjiang, 157013, P.R.China 5. New Star Research Institute of Applied Technology, Hefei, 230031, P.R.China |
| Abstract: | The solution of normal least squares support vector regression(LSSVR)is lack of sparseness,which limits the real-time and hampers the wide applications to a certain degree.To overcome this obstacle,a scheme,named I2FSA-LSSVR,is proposed.Compared with the previously approximate algorithms,it not only adopts the partial reduction strategy but considers the influence between the previously selected support vectors and the willselected support vector during the process of computing the supporting weights.As a result,I2FSA-LSSVR reduces the number of support vectors and enhances the real-time.To confirm the feasibility and effectiveness of the proposed algorithm,experiments on benchmark data sets are conducted,whose results support the presented I2FSA-LSSVR. |
| Keywords: | support vector regression kernel method least squares sparseness |
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