Transactions of Nanjing University of Aeronautics & Astronautics
Improved Scheme for Fast Approximation to Least Squares Support Vector Regression
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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 will-selected 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.
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Supported by the National Natural Science Foundation of China (51006052).
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