A major assumption in many machine learning algorithms is that the training data and testing data have the same distribution. However, in many real-world applications, this assumption may not hold. Transfer learning addresses this problem and utilizes plenty of labeded data in a source domain to solve related but different problems in a target domain. This paper proposed a parameter- transfer learning method based on KMM (Kernel Mean Matching) algorithm. First, we weighed each source instance using KMM and then applied the reweighted instances to the learning method based on parameters. We applied this method to the localization of wireless network. Experiment results have demonstrated that the proposed method outperforms the methods based on instances or parameters, especially when the target training data are relatively few.