Labeling examples that matter: Relevance-based active learning with Gaussian processes

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 8142 LNCS, Issue , 2013, Pages 282-291

  Freytag, Alexander ^a   Rodner, Erik ^a,b   Bodesheim, Paul ^a   Denzler, Joachim ^a  

^a FRIEDRICH SCHILLER UNIVERSITY JENA   (Germany)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACTIVE LEARNING METHODS; BENCHMARK DATASETS; GAUSSIAN PROCESS MODELS; GAUSSIAN PROCESSES; MANUAL ANNOTATION; STATE-OF-THE-ART METHODS; UNSUPERVISED DATA; VISUAL OBJECT RECOGNITION;

GAUSSIAN DISTRIBUTION; PATTERN RECOGNITION;

GAUSSIAN NOISE (ELECTRONIC);

EID: 84886390740     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-40602-7_31     Document Type: Conference Paper

References (19)

1. Baram, Y., El-Yaniv, R., Luz, K.: Online choice of active learning algorithms. JMLR 5, 255-291 (2004)
2. Bernstein, D.S.: Matrix Mathematics, 2nd edn. Princeton University Press (2009)
3. Bodesheim, P., Rodner, E., Freytag, A., Denzler, J.: Divergence-based one-class classification using gaussian processes. In: BMVC, pp. 50.1-50.11 (2012)
4. Campbell, C., Cristianini, N., Smola, A.: Query learning with large margin classifiers. In: ICML (2000)
5. Cebron, N., Berthold, M.: Active learning for object classification: from exploration to exploitation. Data Mining and Knowledge Discovery 18, 283-299 (2009)
6. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR (2009)
7. Ebert, S., Fritz, M., Schiele, B.: Ralf: A reinforced active learning formulation for object class recognition. In: CVPR, pp. 3626-3633 (2012)
8. Freund, Y., Seung, H.S., Shamir, E., Tishby, N.: Selective sampling using the query by committee algorithm. Machine Learning 28, 133-168 (1997)
9. 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)
10. Kapoor, A., Grauman, K., Urtasun, R., Darrell, T.: Gaussian processes for object categorization. IJCV 88, 169-188 (2010)
11. 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)
12. Osugi, T., Kim, D., Scott, S.: Balancing exploration and exploitation: a new algorithm for active machine learning. In: ICDM, pp. 330-337 (2005)
13. Rasmussen, C.E., Williams, C.K.I.: Gaussian Processes for Machine Learning. Adaptive Computation and Machine Learning. The MIT Press (2006)
14. Roy, N., McCallum, A.: Toward optimal active learning through sampling estimation of error reduction. In: ICML, pp. 441-448 (2001)
15. Schohn, G., Cohn, D.: Less is more: Active learning with support vector machines. In: ICML, pp. 839-846 (2000)
16. Seo, S., Wallat, M., Graepel, T., Obermayer, K.: Gaussian process regression: Active data selection and test point rejection. In: IJCNN, pp. 241-246 (2010)
17. Tong, S., Koller, D.: Support vector machine active learning with applications to text classification. JMLR 2, 45-66 (2002)
18. Vezhnevets, A., Buhmann, J.M., Ferrari, V.: Active learning for semantic segmentation with expected change. In: CVPR, pp. 3162-3169 (2012)
19. Yeh, T., Darrell, T.: Dynamic visual category learning. In: CVPR, pp. 1-8 (2008)