A weighted algorithm of inductive transfer learning based on maximum entropy model

Traditional machine learning and data mining algorithms mainly assume that the training and test data must be in the same feature space and follow the same distribution. However, in real applications, these two hypotheses are difficult to hold, traditional algorithms are hence no longer applicable. As a new framework of learning, transfer learning could solve this problem effectively. This paper focuses on one of important branches in this field, namely inductive transfer learning. Correspondingly, a weighted algorithm of inductive transfer learning, based on maximum entropy model, is proposed, called WTLME. It transfers the model parameters learned from the source domain to the target domain, and meanwhile adjusts the weights of instances in the target domain to obtain the model with high accuracy. Extensive studies demonstrate that our proposed algorithm of WTLME is more effective and efficient than traditional algorithms that require learning from scratch if the data distributions change. Moreover, WTLME is comparable to the previous transfer algorithm based on maximum entropy model.