document: Cross domain distribution adaptation via kernel mapping When labeled examples are limited and difficult to obtain , transfer learning employs knowledge from a source domain to improve learning accuracy in the target domain . However , the assumption made by existing approaches , that the marginal and conditional probabilities are directly related between source and target domains , has limited applicability in either the original space or its linear transformations . To solve this problem , we propose an adaptive kernel approach that maps the marginal distribution of target-domain and source-domain data into a common kernel space , and utilize a sample selection strategy to draw conditional probabilities between the two domains closer . We formally show that under the kernel-mapping space , the difference in distributions between the two domains is bounded ; and the prediction error of the proposed approach can also be bounded . Experimental results demonstrate that the proposed method outperforms both traditional inductive classifiers and the state-of-the-art boosting-based transfer algorithms on most domains , including text categorization and web page ratings . In particular , it can achieve around 10 % higher accuracy than other approaches for the text categorization problem . The source code and datasets are available from the authors .
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