Efficient Learning from Few Labeled Examples
Active learning and semi-supervised learning are two approaches to alleviate the burden of labeling large amounts of data. In active learning, user is asked to label the most informative examples in the domain. In semi-supervised learning, labeled data is used together with unlabeled data to boost the performance of learning algorithms. We focus here to combine them together. We first introduce a new active learning strategy, then we propose an algorithm to take the advantage of both active learning and semi-supervised learning. We discuss several advantages of our method. Experimental results show that it is efficient and robust to noise.
KeywordsActive learning Semi-supervised learning Learning from examples Selective sampling Machine learning
Unable to display preview. Download preview PDF.
- 3.Cohn, D.A., Ghahramani, Z., Jordan, M.I.: Active Learning with Statistical Models. In: Advances in Neural Information Processing Systems 7. MIT Press, Cambridge (1995)Google Scholar
- 4.Lewis, D.D., Catlett, J.: Heterogeneous Uncertainty Sampling for Supervised Learning. In: Proceedings of the 11th International Conference on Machine Learning (1994)Google Scholar
- 5.Blum, A., Mitchell, T.: Combining Labeled and Unlabeled Data with Co-Training. In: Proceedings of the 11th Annual Conference on Computational Learning Theory, Madison, WI (1998)Google Scholar
- 7.Blum, A., Chawla, S.: Learning from Labeled and Unlabeled Data Using Graph Mincuts. In: Proceedings of the 18th International Conference on Machine Learning. Morgan Kaufmann, San Francisco (2001)Google Scholar
- 9.Zhu, X., Lafferty, J., Ghahramani, Z.: Combining Active Learning and Semi-supervised Learning Using Gaussian Fields and Harmonic Functions. In: ICML 2003 workshop on the Continuum from Labeled to Unlabeled Data in Machine Learning and Data Mining (2003)Google Scholar
- 10.Muslea, I., Minton, S., Knoblock, C.A.: Active +Semi-supervised Learning Robust Multi-view Learning. In: Proceedings of the 19th International Conference on Machine Learning (2002)Google Scholar
- 11.Wang, W., Zhou, Z.: On Multi-view Active Learning and the Combination with Semi-Supervised Learning. In: Proceedings of the 25th nternational Conference on Machine Learning, Helsinki, Finland (2008)Google Scholar
- 12.Belkin, M., Niyogi, P., Sindhwani, V.: Manifold Regularization: A Geometric Framework for Learning from Examples. Department of Computer Science, University of Chicago, Technical Report (2004)Google Scholar