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Domain adaptation has received much attention as a major form of transfer learning. One issue that should be considered in domain adaptation is the gap between source domain and target domain. In order to improve the generalization ability of domain adaption methods, we proposed a framework for domain adaptation combining source and target data, with a new regularizer which takes generalization bounds into account. This regularization term considers integral probability metric (IPM) as thedoi:10.1155/2016/7046563 pmid:26819589 pmcid:PMC4707017 fatcat:6tmzzozp6zbavp4kocb37ui2bu