Active learning via query synthesis and nearest neighbour search

Neurocomputing

Volumn 147, Issue 1, 2015, Pages 426-434

  Wang, Liantao ^a   Hu, Xuelei ^a,b   Yuan, Bo ^c   Lu, Jianfeng ^a  

^a NANJING UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^b UNIVERSITY OF QUEENSLAND   (Australia)

^c TSINGHUA UNIVERSITY   (China)

Author keywords

Active learning; Kernel function; Pool based sampling; Query synthesis

Indexed keywords

ARTIFICIAL INTELLIGENCE; LAKES; NEAREST NEIGHBOR SEARCH; VIRTUAL REALITY;

ACTIVE LEARNING; ACTIVE-LEARNING ALGORITHM; KERNEL FUNCTION; NEAREST NEIGHBOUR SEARCH; POOL-BASED; SYNTHESIS FEATURES; SYNTHESIS STRATEGY; UNCERTAINTY SAMPLINGS;

LEARNING ALGORITHMS;

ARTICLE; INFORMATION PROCESSING; KERNEL METHOD; LEARNING ALGORITHM; MACHINE LEARNING; MATHEMATICAL COMPUTING; MATHEMATICAL PARAMETERS; NEAREST NEIGHBOUR SEARCH; PRIORITY JOURNAL; QUERY SYNTHESIS; SAMPLING; SUPPORT VECTOR MACHINE;

EID: 84924074550     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2014.06.042     Document Type: Article

References (38)

1. B. Settles, Active learning literature survey, Computer Sciences Technical Report 1648, University of Wisconsin-Madison, 2010.
2. Settles B. Active Learning, Synthesis Lectures on Artificial Intelligence and Machine Learning 2012, Morgan & Claypool Publishers, San Rafael, CA, USA.
3. S. Tong, D. Koller, Support vector machine active learning with applications to text classification, in: ICML, 2000, pp. 999-1006.
4. Zhang L., Chen C., Bu J., Cai D., He X., Huang T.S. Active learning based on locally linear reconstruction. IEEE Trans. Pattern Anal. Mach. Intell. 2011, 33(10):2026-2038.
5. Angluin D. Queries and concept learning. Mach. Learn. 1988, 2(4):319-342.
6. Baum E.B. Neural net algorithms that learn in polynomial time from examples and queries. Trans. Neural Netw. 1991, 2(1):5-19.
7. King R.D., Rowland J., Oliver S.G., Young M., Aubrey W., Byrne E., Liakata M., Markham M., Pir P., Soldatova L.N., Sparkes A., Whelan K.E., Clare A. The automation of science. Science 2009, 324:85-89.
8. L.E. Atlas, D.A. Cohn, R.E. Ladner, Training connectionist networks with queries and selective sampling, in: NIPS, 1989, pp. 566-573.
9. S. Dasgupta, D. Hsu, C. Monteleoni, A general agnostic active learning algorithm, in: ISAIM, 2008.
10. D.D. Lewis, W.A. Gale, A sequential algorithm for training text classifiers, in: Proceedings of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Springer-Verlag New York, Inc., New York, NY, USA, 1994, pp. 3-12.
11. B. Settles, M. Craven, An analysis of active learning strategies for sequence labeling tasks, in: Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2008, pp. 1070-1079.
12. S.-J. Huang, R. Jin, Z.-H. Zhou, Active learning by querying informative and representative examples, in: NIPS, 2010, pp. 892-900.
13. P. Melville, R.J. Mooney, Diverse ensembles for active learning, in: Proceedings of the 21st International Conference on Machine Learning, ACM, New York, NY, USA, 2004, pp. 74-81.
14. K. Lang, E. Baum, Query learning can work poorly when a human oracle is used, in: Proceedings of the IEEE International Joint Conference on Neural Networks, 1992, pp. 335-340.
15. X. Hu, L. Wang, B. Yuan, Querying representative points from a pool based on synthesized queries, in: Proceedings of the International Joint Conference on Neural Networks, 2012, pp. 1734-1739.
16. P. Indyk, R. Motwani, Approximate nearest neighbors: towards removing the curse of dimensionality, in: Proceedings of the 13th Annual ACM Symposium on Theory of Computing, 1998, pp. 604-613.
17. Schölkopf B., Smola A.J., Müller K.-R. Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput. 1998, 10(5):1299-1319.
18. Dekel O., Gentile C., Sridharan K. Selective sampling and active learning from single and multiple teachers. J. Mach. Learn. Res. 2012, 13(9):2655-2697.
19. Yu K., Ji L., Zhang X. Kernel nearest-neighbor algorithm. Neural Process. Lett. 2002, 15(2):147-156.
20. Shawe-Taylor J., Cristianini N. Kernel Methods for Pattern Analysis 2004, Cambridge University Press, Cambridge, UK.
21. D. Angluin, Queries revisited, in: Proceedings of the International Conference on Algorithmic Learning Theory, 2001, pp. 12-31.
22. King R., Whelan K., Jones F., Reiser P., Bryant C., Muggleton S., Kell D., Oliver S. Functional genomic hypothesis generation and experimentation by a robot scientist. Nature 2004, 427(5):247-252.
23. Hoi S.C.H., Jin R., Lyu M.R. Batch mode active learning with applications to text categorization and image retrieval. IEEE Trans. Knowl. Data Eng. 2009, 21(9):1233-1247.
24. X.-Y. Wei, Z.-Q. Yang, Coached active learning for interactive video search, in: Proceedings of the ACM International Conference on Multimedia, 2011, pp. 443-452.
25. X. Li, Y. Guo, Adaptive active learning for image classification, in: Proceedings of the 26th IEEE Conference on Computer Vision and Pattern Recognition, 2013, pp. 1-8.
26. Jones S., Shao L., Du K. Active learning for human action retrieval using query pool selection. Neurocomputing 2014, 124:89-96.
27. Tong S., Koller D. Support vector machine active learning with applications to text classification. J. Mach. Learn. Res. 2001, 3:45-66.
28. Lindenbaum M., Markovitch S., Rusakov D. Selective sampling for nearest neighbor classifiers. Mach. Learn. 2004, 54(2):125-152.
29. H.S. Seung, M. Opper, H. Sompolinsky, Query by committee, in: Proceedings of the ACM Workshop on Computational Learning Theory, 1992, pp. 287-294.
30. N. Abe, H. Mamitsuka, Query learning strategies using boosting and bagging, in: Proceedings of the 15th International Conference on Machine Learning, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1998, pp. 1-9.
31. N. Roy, A. Mccallum, Toward optimal active learning through sampling estimation of error reduction, in: Proceedings of the 18th International Conference on Machine Learning, Morgan Kaufmann, San Francisco, CA, USA, 2001, pp. 441-448.
32. Y. Guo, R. Greiner, Optimistic active learning using mutual information, in: International Joint Conference on Artificial Intelligence (IJCAI), 2007, pp. 823-829.
33. H.T. Nguyen, A. Smeulders, Active learning using pre-clustering, in: Proceedings of the 21st International Conference on Machine Learning, ACM Press, New York, NY, USA, 2004, pp. 623-630.
34. S. Dasgupta, D. Hsu, Hierarchical sampling for active learning, in: Proceedings of the 25th International Conference on Machine Learning, ACM, New York, NY, USA, 2008, pp. 208-215.
35. S.C. Hoi, R. Jin, J. Zhu, M.R. Lyu, Semi-supervised svm batch mode active learning for image retrieval, in: IEEE Conference on Computer Vision and Pattern Recognition, 2008. CVPR 2008, IEEE, Piscataway, NJ, United States, 2008, pp. 1-7.
36. A. Frank, A. Asuncion, UCI machine learning repository, 2010. URL . http://www.archive.ics.uci.edu/ml.
37. Y. LeCun, et al., Comparison of learning algorithms for handwritten digit recognition, in: Proceedings of the International Conference on Artificial Neural Networks, 1995, pp. 53-60.
38. Chang C.-C., Lin C.-J. LIBSVM. a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2011, 2:1-27.