关键词: 迁移学习, 数据缺失, 支持向量回归机, 知识相关性, 信息修补

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.

Key words: transfer learning, data missing, Supported Vector Regression (SVR), knowledge similarity, information remedy