Abstract
Active learning is an essential tool to reduce manual annotation costs in the presence of large amounts of unsupervised data. In this paper, we introduce new active learning methods based on measuring the impact of a new example on the current model. This is done by deriving model changes of Gaussian process models in closed form. Furthermore, we study typical pitfalls in active learning and show that our methods automatically balance between the exploitation and the exploration trade-off. Experiments are performed with established benchmark datasets for visual object recognition and show that our new active learning techniques are able to outperform state-of-the-art methods.
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
Baram, Y., El-Yaniv, R., Luz, K.: Online choice of active learning algorithms. JMLR 5, 255–291 (2004)
Bernstein, D.S.: Matrix Mathematics, 2nd edn. Princeton University Press (2009)
Bodesheim, P., Rodner, E., Freytag, A., Denzler, J.: Divergence-based one-class classification using gaussian processes. In: BMVC, pp. 50.1–50.11 (2012)
Campbell, C., Cristianini, N., Smola, A.: Query learning with large margin classifiers. In: ICML (2000)
Cebron, N., Berthold, M.: Active learning for object classification: from exploration to exploitation. Data Mining and Knowledge Discovery 18, 283–299 (2009)
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR (2009)
Ebert, S., Fritz, M., Schiele, B.: Ralf: A reinforced active learning formulation for object class recognition. In: CVPR, pp. 3626–3633 (2012)
Freund, Y., Seung, H.S., Shamir, E., Tishby, N.: Selective sampling using the query by committee algorithm. Machine Learning 28, 133–168 (1997)
Freytag, A., Rodner, E., Bodesheim, P., Denzler, J.: Rapid uncertainty computation with gaussian processes and histogram intersection kernels. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds.) ACCV 2012, Part II. LNCS, vol. 7725, pp. 511–524. Springer, Heidelberg (2013)
Kapoor, A., Grauman, K., Urtasun, R., Darrell, T.: Gaussian processes for object categorization. IJCV 88, 169–188 (2010)
Kemmler, M., Rodner, E., Denzler, J.: One-class classification with gaussian processes. In: Kimmel, R., Klette, R., Sugimoto, A. (eds.) ACCV 2010, Part II. LNCS, vol. 6493, pp. 489–500. Springer, Heidelberg (2011)
Osugi, T., Kim, D., Scott, S.: Balancing exploration and exploitation: a new algorithm for active machine learning. In: ICDM, pp. 330–337 (2005)
Rasmussen, C.E., Williams, C.K.I.: Gaussian Processes for Machine Learning. Adaptive Computation and Machine Learning. The MIT Press (2006)
Roy, N., McCallum, A.: Toward optimal active learning through sampling estimation of error reduction. In: ICML, pp. 441–448 (2001)
Schohn, G., Cohn, D.: Less is more: Active learning with support vector machines. In: ICML, pp. 839–846 (2000)
Seo, S., Wallat, M., Graepel, T., Obermayer, K.: Gaussian process regression: Active data selection and test point rejection. In: IJCNN, pp. 241–246 (2010)
Tong, S., Koller, D.: Support vector machine active learning with applications to text classification. JMLR 2, 45–66 (2002)
Vezhnevets, A., Buhmann, J.M., Ferrari, V.: Active learning for semantic segmentation with expected change. In: CVPR, pp. 3162–3169 (2012)
Yeh, T., Darrell, T.: Dynamic visual category learning. In: CVPR, pp. 1–8 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Freytag, A., Rodner, E., Bodesheim, P., Denzler, J. (2013). Labeling Examples That Matter: Relevance-Based Active Learning with Gaussian Processes. In: Weickert, J., Hein, M., Schiele, B. (eds) Pattern Recognition. GCPR 2013. Lecture Notes in Computer Science, vol 8142. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40602-7_31
Download citation
DOI: https://doi.org/10.1007/978-3-642-40602-7_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40601-0
Online ISBN: 978-3-642-40602-7
eBook Packages: Computer ScienceComputer Science (R0)