Title :
Extending Semi-supervised Learning Methods for Inductive Transfer Learning
Author :
Shi, Yuan ; Lan, Zhenzhong ; Liu, Wei ; Bi, Wei
Author_Institution :
Sch. of Software, Sun Yat-sen Univ. Guangzhou, Guangzhou, China
Abstract :
Inductive transfer learning and semi-supervised learning are two different branches of machine learning. The former tries to reuse knowledge in labeled out-of-domain instances while the later attempts to exploit the usefulness of unlabeled in-domain instances. In this paper, we bridge the two branches by pointing out that many semi-supervised learning methods can be extended for inductive transfer learning, if the step of labeling an unlabeled instance is replaced by re-weighting a diff-distribution instance. Based on this recognition, we develop a new transfer learning method, namely COITL, by extending the co-training method in semi-supervised learning. Experimental results reveal that COITL can achieve significantly higher generalization and robustness, compared with two state-of-the-art methods in inductive transfer learning.
Keywords :
learning (artificial intelligence); cotraining method; diff-distribution instance; inductive transfer learning; machine learning; re-weighting instance; semisupervised learning methods; Computer science; Data mining; Labeling; Learning systems; Machine learning; Robustness; Semisupervised learning; Sun; Training data; Web pages; Inductive transfer learning; co-training; semi-supervised learning;
Conference_Titel :
Data Mining, 2009. ICDM '09. Ninth IEEE International Conference on
Conference_Location :
Miami, FL
Print_ISBN :
978-1-4244-5242-2
Electronic_ISBN :
1550-4786
DOI :
10.1109/ICDM.2009.75