SHI Yingzhong1,2,WANG Shitong2,JIANG Yizhang2,LIU Peilin1,2
1. College of Internet of Things, Wuxi Institute of Technology, Wuxi Jiangsu 214121,China 2. School of Digital Media, Jiangnan University, Wuxi Jiangsu 214122, China;
Abstract：The classical regression systems modeling methods suppose that the training data are sufficient, but partial information missing may weaken the generalization abilities of the regression systems constructed based on this dataset. In order to solve this problem, a regression system with the transfer learning abilities, i.e. Transfer learning Support Vector Regression (T-SVR for brevity) was proposed based on support vector regression. T-SVR could use the current data information sufficiently, and learn from the existing useful historical knowledge effectively, so that remedy the information lack in the current scene. Reinforced current model was obtained through controlling the similarity between current model and history model in the object function and current model can benefit from history scene when information is missing or insufficient. The experiments on simulation data and real data show that T-SVR has better adaptability than the traditional regression modeling method in the scene with information missing.
史荧中 王士同 蒋亦樟 刘培林. 迁移学习支持向量回归机[J]. 计算机应用, 2013, 33(11): 3084-3089.
SHI Yingzhong WANG Shitong JIANG Yizhang LIU Peilin. Transfer learning support vector regression. Journal of Computer Applications, 2013, 33(11): 3084-3089.
2013, Vol. 33 
