Abstract
Transfer learning, which is one of the most important research directions in machine learning, has been studied in various fields in recent years. In this paper, we combine the theories of multi-source and multi-view learning into transfer learning and propose a new algorithm named Multi-source Transfer Learning with Multi-view Adaboost (MsTL-MvAdaboost). Different from many previous works on transfer learning, in this algorithm, we not only use the labeled data from several source tasks to help learn one target task, but also consider how to transfer them in different views synchronously. We regard all the source and target tasks as a collection of several constituent views and each of these tasks can be learned from different views. Experimental results also validate the effectiveness of our proposed approach.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering 22(10), 1345–1359 (2009)
Dai, W., Yang, Q., Xue, G.R., Yu, Y.: Boosting for transfer learning. In: Proceedings of the 24th International Conference on Machine Learning, pp. 193–200 (2007)
Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Science 55, 119–139 (1997)
Xu, Z., Sun, S.: An algorithm on multi-view adaboost. In: Proceedings of 17th International Conference on Neural Information Processing, pp. 355–362 (2010)
Perkins, D.N., Salomon, G.: Transfer of learning. The Journal of International Encyclopedia of Education 2, 10 (1992)
Yao, Y., Doretto, G.: Boosting for transfer learning with multiple sources. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1855-1862 (2010)
Xu, Z., Sun, S.: Multi-view Transfer Learning with Adaboost. In: Proceedings of the 23rd IEEE International Conference on Tools with Artificial Intelligence, pp. 399–402 (2011)
Sun, S., Jin, F., Tu, W.: View Construction for Multi-view Semi-supervised Learning. In: Liu, D., Zhang, H., Polycarpou, M., Alippi, C., He, H. (eds.) ISNN 2011, Part I. LNCS, vol. 6675, pp. 595–601. Springer, Heidelberg (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Xu, Z., Sun, S. (2012). Multi-source Transfer Learning with Multi-view Adaboost. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7665. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34487-9_41
Download citation
DOI: https://doi.org/10.1007/978-3-642-34487-9_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34486-2
Online ISBN: 978-3-642-34487-9
eBook Packages: Computer ScienceComputer Science (R0)