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 couldnt 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.
transfer learning machine learning clustering document categorization
Wei Sun Qian Xu
College of Mechanical Electronic & Information Engineering China University of Mining & Technology(Beijing) Beijing,China
国际会议
杭州
英文
528-531
2012-03-23（万方平台首次上网日期，不代表论文的发表时间）