Rough ν-support vector regression |
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| Authors: | Yongping Zhao Jianguo Sun |
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| Affiliation: | 1. College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, PR China;2. BioCircuits Institute, University of California San Diego, La Jolla, CA 92093-0404, USA;1. School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China;2. AVIC Aeroengine Control Research Institute, Wuxi 214063, China;1. Saarland University, Faculty of Mathematics and Computer Science, 66041 Saarbrücken, Germany;2. Brandenburg University of Technology, Faculty of Mathematics, Natural Sciences and Computer Science, 03046 Cottbus, Germany;1. College of Mathematics and Statistics, Northwest Normal University, Lanzhou 730070, PR China;2. College of Technology and Engineering, Lanzhou University of Technology, Lanzhou 730050, PR China;1. School of Electronic Information Engineering, Tianjin University, 300072 Tianjin, China;2. Research Institute for Nanodevice and Bio Systems, Hiroshima University, 1-4-2 Kagamiyama, Higashi-hiroshima, Hiroshima 739-8527, Japan |
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| Abstract: | After combining the classical ν-SVR with the rough theory, we propose a rough ν-SVR. Double εs are utilized to construct the rough margin for rough ν-SVR instead of single ε for the classical ν-SVR, and this rough margin consisting of positive region, boundary region, and negative region yields the feasible set of the rough ν-SVR larger than that of the classical ν-SVR, which makes the objective function of the rough ν-SVR not more than that of the classical ν-SVR. This may lead to the improvement of the performance. Meantime, experimental results on benchmark data sets confirm the validation and feasibility of our proposed rough ν-SVR. |
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