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Cross domain distribution adaptation via kernel mapping
2009
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '09
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
doi:10.1145/1557019.1557130
dblp:conf/kdd/ZhongFPZRTV09
fatcat:mghl6dcxffbgpmygfl34edg5ay