基于KMM匹配的参数迁移学习算法

当训练数据和测试数据来自不同的领域或任务以至于训练数据和测试数据的分布不相同时，需要进行知识的迁移.本文提出一种基于实例KMM匹配的参数迁移学习方法.利用KMM算法估计每个源领域实例的权重，再利用得到的权重，把这些实例应用到基于参数的迁移学习方法中.把该迁移学习算法应用到无线网络定位问题中时，该方法的定位准确度要高于单独从实例或是从参数出发的迁移学习方法.

KMM-based Learning Algorithm for Parameter Transfer

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.