Abstract
Active learning is a learning mechanism which can actively query the user for labels. The goal of an active learning algorithm is to build an effective training set by selecting those most informative samples and improve the efficiency of the model within the limited time and resource. In this paper, we mainly focus on a state-of-the-art active learning method, the SVM-based margin sampling. However, margin sampling does not consider the distribution and the structural space connectivity among the unlabeled data when several examples are chosen simultaneously, which may lead to oversampling on dense regions. To overcome this shortcoming, we propose an improved margin sampling method by applying the manifold-preserving graph reduction algorithm to the original margin sampling method. Experimental results on multiple data sets demonstrate that our method obtains better classification performance compared with the original margin sampling.
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.
Similar content being viewed by others
References
Boser, B.E., Guyou, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: 5th Workshop on Computational Learning Theory, Pittsburgh, pp. 144–152 (1992)
Campbell, C., Cristianini, N., Smola, A.: Query learning with large margin classifiers. In: 17th International Conference on Machine Learning, Stanford, pp. 111–118 (2000)
Cohn, D., Atlas, L., Ladner, R.: Improving generalization with active learning. Machine Learning 15, 201–221 (1994)
Ferecatu, M., Boujemaa, N.: Interactive remote-sensing image retrieval image retrieval. IEEE Transactions on Geoscience Remote Sensing 45, 818–826 (2007)
Freund, Y., Seung, H.S., Shamir, E., Tishby, N.: Selective sampling using the query by committee algorithm. Machine Learning 28, 133–168 (1997)
Hern\(\acute{a}\)ndez, E.P., Ambroladze, A., Taylor, J.S., Sun, S.: PAC-Bayes bounds with data dependent priors. The Journal of Machine Learning Research 13, 3507–3531 (2012)
Huang, S., Jin, R., Zhou, Z.: Active learning by querying informative and representative examples. In: 24th Annual Conference on Neural Information Processing Systems, Vancouver, pp. 892–900 (2010)
Kapoor, A., Grauman, K., Urtasun, R., Darrell, T.: Active learning with Gaussian processed for object categorization. In: 11th International Conference on Computer Vision, Rio de Janeiro, pp. 1–8 (2007)
Mackay, D.J.C.: Information-based objective functions for active data selection. Neural Computation 4, 590–604 (1992)
Nguyen, H.T., Smeulders, A.: Active learning using pre-clustering. In: 21st International Conference on Machine Learning, Banff, Canada, pp. 623–630 (2004)
Oskoei, M.A., Hu, H.: Support vector machine-based classification scheme for myoelectric control applied to upper limb. IEEE Transactions on Biomedical Engineering 55, 1956–1965 (2008)
Sch\(\ddot{o}\)lkopf, B., Smola, A.J.: Learning with Kernels. MIT press, Cambridge (2002)
Schohn, G., Cohn, D.: Less is more: Active learning with support vectors machines. In: 17th International Conference on Machine Learning, Stanford, pp. 839–846 (2000)
Silva, C., Ribeiro, B.: Margin-based active learning and background knowledge in text mining. In: 4th International Conference on Hybird Intelligent Systems, Washington, pp. 8–13 (2004)
Sun, S., Hussain, Z., Taylor, J.S.: Manifold-preserving graph reduction for sparse semi-supervised learning. Neurocomputing 124, 13–21 (2013)
Sun, S., Hardoon, D.: Active learning with extremely sparse labeled examples. Neurocomputing 73, 2980–2988 (2010)
Tuia, D., Ratle, F., Pacifici, F., Kanevski, M.F., Emery, W.J.: Active learning methods for remote sensing image classification. IEEE Transactions on Geoscience Remote Sensing 47, 2218–2232 (2009)
Zhang, Q., Sun, S.: Multiple-view multiple-learner active learning. Pattern Recognition 43, 3113–3119 (2010)
Zhou, J., Sun, S.: Active learning of Gaussian processes with manifold-preserving graph reduction. Neural Computing & Applications (2014), doi:10.1007/s00521-014-1643-8
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhou, J., Sun, S. (2014). Improved Margin Sampling for Active Learning. In: Li, S., Liu, C., Wang, Y. (eds) Pattern Recognition. CCPR 2014. Communications in Computer and Information Science, vol 483. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45646-0_13
Download citation
DOI: https://doi.org/10.1007/978-3-662-45646-0_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-45645-3
Online ISBN: 978-3-662-45646-0
eBook Packages: Computer ScienceComputer Science (R0)