Abstract
We often face the situation where very limited labeled data are available to learn an effective classifier in target domain while there exist large amounts of labeled data with similar feature or distribution in certain relevant domains. Transfer learning aims at improving the performance of a learner in target domain given labeled data in one or more source domains. In this paper, we present an algorithm to learn effective classifier without or a few labeled data on target domain, given some labeled data with same features and similar distribution in source domain. Our algorithm uses the data edit technique to approach distribution from the source domain to the target domain by removing “mismatched” examples in source domain and adding “matched” examples in target domain. Experimental results on email classification problem have confirmed the effectiveness of the proposed algorithm.
This work is supported by the young teacher project in BUCT under contract (Number QN0824).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Dai, W., Yang, Q., Xue, G.-R., Yu, Y.: Boosting for transfer learning. In: Ghahramani, Z. (ed.) Proceedings of the 24th International Conference on Machine Learning (ICML 2007), June 20-24, pp. 193–200. ACM, New York (2007)
Jialin, S., Yang, Q.: A survey on transfer learning. Technical Report HKUST-CS08-08, Hong Kong University of Science and Technology (2008)
Liao, X., Xue, Y., Carin, L.: Logistic regression with an auxiliary data source. In: Raedt, L.D., Wrobel, S. (eds.) Proceedings of the 22nd International Conference on Machine Learning (ICML 2005), August 7-11, pp. 505–512. ACM, New York (2005)
Ling, X., Dai, W., Xue, G.-R., Yang, Q., Yu, Y.: Spectral domain-transfer learning. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Las Vegas, Nevada, August 2008, pp. 488–496 (2008)
Wu, P., Dietterich, T.G.: Improving svm accuracy by training on auxiliary data sources. In: Brodley, C.E. (ed.) Proceedings of the 21st International Conference on Machine Learning (ICML 2004), July 4-8. ACM, New York (2004)
Zighed, D.A., Lallich, S., Muhlenbach, F.: Separability index in supervised learning. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) PKDD 2002. LNCS, vol. 2431, pp. 475–487. Springer, Heidelberg (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cheng, Y., Li, Q. (2009). Transfer Learning with Data Edit. In: Huang, R., Yang, Q., Pei, J., Gama, J., Meng, X., Li, X. (eds) Advanced Data Mining and Applications. ADMA 2009. Lecture Notes in Computer Science(), vol 5678. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03348-3_41
Download citation
DOI: https://doi.org/10.1007/978-3-642-03348-3_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03347-6
Online ISBN: 978-3-642-03348-3
eBook Packages: Computer ScienceComputer Science (R0)