Abstract
Semi-supervised learning and active learning are important techniques to solve the shortage of labeled examples. In this paper, a novel active learning algorithm combining semi-supervised Learning with Local and Global Consistency (LLGC) is proposed. It selects the example that can minimize the estimated expected classification risk for labeling. Then, a better classifier can be trained with labeled data and unlabeled data using LLGC. The experiments on two datasets demonstrate the effectiveness of the proposed algorithm.
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References
Zhu, X.: Semi-supervised learning literature survey. Computer Science 2, 3 (2006)
Settles, B.: Active learning literature survey. University of Wisconsin, Madison (2010)
Ji, M., Han, J.: A variance minimization criterion to active learning on graphs. In: International Conference on Artificial Intelligence and Statistics, pp. 556–564 (2012)
Settles, B., Craven, M., Ray, S.: Multiple-instance active learning. In: Advances in Neural Information Processing Systems, NIPS, pp. 1289–1296. MIT Press (2008)
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, pp. 58–65 (2003)
Guo, Y., Greiner, R.: Optimistic active-learning using mutual information. In: IJCAI, vol. 7, pp. 823–829 (2007)
Chakraborty, S., Balasubramanian, V., Panchanathan, S.: Dynamic batch mode active learning. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2649–2656. IEEE (2011)
Zhou, D., Bousquet, O., Lal, T.N., Weston, J., Schölkopf, B.: Learning with local and global consistency. In: NIPS, vol. 16, pp. 321–328 (2003)
Demir, B., Persello, C., Bruzzone, L.: Batch-mode active-learning methods for the interactive classification of remote sensing images. IEEE Transactions on Geoscience and Remote Sensing 49(3), 1014–1031 (2011)
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Gu, Y., Jin, Z., Chiu, S.C. (2014). Combining Active Learning and Semi-supervised Learning Using Local and Global Consistency. In: Loo, C.K., Yap, K.S., Wong, K.W., Teoh, A., Huang, K. (eds) Neural Information Processing. ICONIP 2014. Lecture Notes in Computer Science, vol 8834. Springer, Cham. https://doi.org/10.1007/978-3-319-12637-1_27
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DOI: https://doi.org/10.1007/978-3-319-12637-1_27
Publisher Name: Springer, Cham
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