A Transfer Learning Algorithm for Document Categorization Based on Clustering

Traditional machine learning and data mining have achieved significant success in many knowledge engineering areas including classification, regression clustering and so on, but a major assumption in them is that the training and test data must be in the same feature space and follow the same distribution. However, in real applications, this assumption couldn´t be satisfied for ever. In this case, the role of transfer learning can be highlight, because transfer learning does not make the same distributional assumptions as the traditional machine learning, and reduces the dependencies of the target task and training data, has a wider migration of knowledge. In this paper we will propose a transfer learning algorithm for document categorization based on clustering. We describe the main idea and the step of the algorithm. Then use experiment to test the algorithm and compare the algorithm with no-transfer algorithm. the experiment demonstrate that the algorithm we proposed in this paper is better than the others in some extent.