The homogeneity, or more generally, the similarity between source domains and a target domain seems to be essential to a positive transfer learning. In practice, however, the similarity condition is difficult to check and is often violated. In this paper, instead of the popularly used similarity condition, a seeming similarity is introduced, which is defined by a non-orthogonality together with a smoothness. Such a condition is naturally satisfied under common situations and even implies the dissimilarity in some sense. Based on the seeming similarity together with an $L_2$-adjustment, a source-function weighted-transfer learning estimation (sw-TLE) is constructed. By source-function weighting, an adaptive transfer learning is achieved in the sense that it is applied to similar and dissimilar scenarios with a relatively high estimation efficiency. Particularly, under the case with homogenous source and target models, the sw-TLE even can be competitive with the full data estimator. The hidden relationship between the source-function weighting estimator and the James-Stein estimator is established as well, which reveals the structural reasonability of our methodology. Moreover, the strategy does apply to nonparametric and semiparametric models. The comprehensive simulation studies and real data analysis can illustrate that the new strategy is significantly better than the competitors.
翻译:源域和目标域之间的相似性,或更笼统地说,对于积极的转移学习来说,源域和目标域之间的相似性似乎至关重要。但在实践中,相似性条件难以核实,而且经常被违反。在本文件中,与普遍使用的相似性条件相反,引入了似乎相似的相似性,其定义是非同一性加上平滑性。在共同情况下,这种条件自然地得到满足,甚至意味着某种意义上的差异性。基于似乎与正转移学习的相似性,加上2美元调整,一种源函数加权转移学习估计(sw-TLE)是构建的。根据源功能加权估计(sw-TLE),适应性转移学习的实现,其意义在于它适用于类似和不同的情况,其估计效率相对较高。特别是,在同源和目标模型的情况下,Sw-TLEE甚至可以与全部数据估测者具有竞争力。根据源计算器计算重量的计算器和James-Sestorator 之间的隐藏关系是构建的。通过源函数加权加权加权估算,因此,采用新的结构分析是更精确的模型,这可以说明新的分析方法。