Active learning with support vector machines

Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery

Volumn 4, Issue 4, 2014, Pages 313-326

  Kremer, Jan ^a   Steenstrup Pedersen, Kim ^a   Igel, Christian ^a  

^a UNIVERSITY OF COPENHAGEN   (Denmark)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; LEARNING ALGORITHMS;

ACTIVE-LEARNING ALGORITHM; CONFLICT OF INTEREST; CONFLICTS OF INTEREST; DATA MINING APPLICATIONS; DECISION BOUNDARY; HUMAN ANNOTATIONS; LINEAR CLASSIFICATION; MATHEMATICAL PROPERTIES;

SUPPORT VECTOR MACHINES;

EID: 84906504171     PISSN: 19424787     EISSN: 19424795     Source Type: Journal    
DOI: 10.1002/widm.1132     Document Type: Article

References (50)

1. Demir B, Bruzzone L. A novel active learning method for support vector regression to estimate biophysical parameters from remotely sensed images. In: Bruzzone L, ed. Proceedings of SPIE 8537, Image and Signal Processing for Remote Sensing XVIII, volume 8537, International Society for Optics and Photonics, 85370L, Edinburgh, UK; 2012.
2. Brinker K. Active learning of label ranking functions. In: Proceedings of the International Conference on Machine Learning (ICML). Banff, Canada: ACM Press; 2004, 129-136.
3. Yu H. SVM selective sampling for ranking with application to data retrieval. In: Proceedings of the International Conference on Knowledge Discovery in Data Mining (SIGKDD). Chicago, IL: ACM Press; 2005, 354-363.
4. Settles B. Active Learning. San Rafael, CA: Morgan & Claypool; 2012.
5. Olsson F. A literature survey of active machine learning in the context of natural language processing. Technical report, Swedish Institute of Computer Science, 2009.
6. Shi X, Fan W, Ren J. Actively transfer domain knowledge. In: Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD). Antwerp, Belgium: Springer; 2008, 342-357.
7. Richards JW, Starr DL, Brink H, Miller AA, Bloom JS, Butler NR, James JB, Long JP, Rice J. Active learning to overcome sample selection bias: application to photometric variable star classification. Astrophys J 2012, 744:192.
8. Quionero-Candela J, Sugiyama M, Schwaighofer A, Lawrence N. Dataset Shift in Machine Learning. Cambridge, MA: MIT Press; 2009.
9. Boser B, Guyon I, Vapnik V. A training algorithm for optimal margin classifiers. In: Workshop on Computational Learning Theory (COLT). Pittsburgh, PA: ACM Press; 1992, 144-152.
10. Cortes C, Vapnik V. Support-vector networks. Mach Learn 1995, 20:273-297.
11. Mammone A, Turchi M, Cristianini N. Support vector machines. WIREs Comput Stat 2009, 1:283-289.
12. Salcedo-Sanz S, Rojo-Álvarez JL, Martnez-Ramón M, Camps-Valls G. Support vector machines in engineering: an overview. WIREs Data Min Knowl Discov 2014, 4:234-267.
13. Schölkopf B, Smola A. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Cambridge, MA: MIT Press; 2002.
14. Shawe-Taylor J, Cristianini N. Kernel Methods for Pattern Analysis. Cambridge, UK: Cambridge University Press; 2004.
15. Steinwart I, Christmann A. Support Vector Machines. Berlin/Heidelberg, Germany: Springer; 2008.
16. Aronszajn N. Theory of reproducing kernels. Trans Am Math Soc 1950, 68:337-404.
17. Campbell C, Cristianini N, Smola A. Query learning with large margin classifiers. In: Proceedings of the International Conference on Machine Learning (ICML). Stanford, CA: Morgan Kaufmann; 2000, 111-118.
18. Lewis D, Gale W. A sequential algorithm for training text classifiers. In: Proceedings of the SIGIR Conference on Research and Development in Information Retrieval (SIGIR). Dublin, Ireland: ACM Press; 1994.
19. Mitchell TM. Generalization as search. Artif Intell 1982, 18:203-226.
20. Balcan M-F, Beygelzimer A, Langford J. Agnostic active learning. In: Proceedings of the International Conference on Machine Learning (ICML). Pittsburgh, PA: ACM Press; 2006, 65-72.
21. Tong S. Active learning: Theory and applications. PhD Thesis, Stanford University, 2001.
22. Schohn G, Cohn D. Less is more: Active learning with support vector machines. In: Proceedings of the International Conference on Machine Learning (ICML). Stanford, CA: Morgan Kaufmann; 2000, 839-846.
23. Tong S, Koller D. Support vector machine active learning with applications to text classification. J Mach Learn Res 2002, 2:45-66.
24. Guyon I, Cawley G, Dror G, Lemaire V. Results of the active learning challenge. J Mach Learn Res 2011, 16:19-45.
25. Ho C-H, Tsai M-H, Lin C-J. Active learning and experimental design with SVMs. J Mach Learn Res 2011, 16:71-84.
26. Dasgupta S. Two faces of active learning. Theor Comput Sci 2011, 412:1767-1781.
27. Xu Z, Yu K, Tresp V, Xu X, Wang J. Representative sampling for text classification using support vector machines. In: Proceedings of the European Conference on Information Retrieval (ECIR). Pisa, Italy: Springer; 2003, 393-407.
28. Dasgupta S, Hsu D. Hierarchical sampling for active learning. In: Proceedings of the International Conference on Machine Learning (ICML). Helsinki, Finland: ACM Press; 2008, 208-215.
29. Huang S-J, Jin R, Zhou Z-H. Active learning by querying informative and representative examples. In: Advances in Neural Information Processing Systems (NIPS). Vancouver, Canada: MIT Press; 2010, 892-900.
30. Hoi S, Jin R, Zhu J, Lyu M. Semi-supervised SVM batch mode active learning for image retrieval. In: Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR). Anchorage, AK: IEEE; 2008, 1-7.
31. Li C-L, Ferng C-S, Lin H-T. Active learning with hinted support vector machine. J Mach Learn Res 2012, 25:221-235.
32. Chapelle O, Schölkopf B, Zien A, eds. Semi-Supervised Learning. Cambridge, MA: MIT Press; 2006.
33. Mitra P, Murthy C, Pal S. A probabilistic active support vector learning algorithm. Trans Pattern Anal Mach Intell 2004, 26:413-418.
34. Cortes C, Mohri M, Riley M, Rostamizadeh A. Sample selection bias correction theory. In: Bshouty N, Stoltz G, Vayatis N, Zeugmann T, eds. Algorithmic Learning Theory. Berlin/Heidelberg, Germany: Springer; 2008, 38-53.
35. Zadrozny B, Langford J, Abe N. Cost-sensitive learning by cost-proportionate example weighting. In: Proceedings of the International Conference on Data Mining (ICDM). Melbourne, FL: IEEE; 2003, 435-442.
36. Beygelzimer A, Dasgupta S, Langford J. Importance weighted active learning. In: Proceedings of the International Conference on Machine Learning (ICML). Montreal, Canada: ACM Press; 2009, 49-56.
37. Beygelzimer A, Langford J, Tong Z, Hsu D. Agnostic active learning without constraints. In: Advances in Neural Information Processing Systems (NIPS). Vancouver, Canada: MIT Press; 2010, 199-207.
38. Ganti R, Gray A. Upal: Unbiased pool based active learning. In: Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS). La Palma, Canary Islands; 2012, 422-431.
39. Zhao L, Sukthankar G, Sukthankar R. Importance-weighted label prediction for active learning with noisy annotations. In: Proceedings of the International Conference on Pattern Recognition (ICPR). Tsukuba, Japan: IEEE; 2012, 3476-3479.
40. Platt J. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In: Smola A, Bartlett P, Schölkopf B, Schuurmans D, eds. Advances in Large Margin Classifiers. Cambridge, MA: MIT Press; 1999, 61-74.
41. Hastie T, Tibshirani R. Classification by pairwise coupling. Ann Stat 1998, 26:451-471.
42. Platt J, Cristianini N, Shawe-Taylor J. Large margin DAGs for multiclass classification. Adv Neural Inf Process Syst 2000, 12:547-553.
43. Rifkin R, Klautau A. In defense of one-vs-all classification. J Mach Learn Res 2004, 5:101-141.
44. Vapnik V. Statistical Learning Theory. Hoboken, NJ: John Wiley and Sons; 1998.
45. Luo T, Kramer K, Goldgof D, Hall L, Samson S, Remsen A, Hopkins T. Active learning to recognize multiple types of plankton. In: Proceedings of the International Conference on Pattern Recognition (ICPR), volume 3. Cambridge, UK: IEEE; 2004, 478-481.
46. Bordes A, Ertekin S, Weston J, Bottou L. Fast kernel classifiers with online and active learning. J Mach Learn Res 2005, 6:1579-1619.
47. Glasmachers T, Igel C. Second-order SMO improves SVM online and active learning. Neural Comput 2008, 20:374-382.
48. Platt J. Fast training of support vector machines using sequential minimal optimization. In: Schölkopf B, Burges C, Smola A, eds. Advances in Kernel Methods-Support Vector Learning chapter 12. Cambridge, MA: MIT Press; 1999, 185-208.
49. Tong S, Chang E. Support vector machine active learning for image retrieval. In: Proceedings of the International Conference on Multimedia (MM). Ottawa, Canada: ACM Press; 2001, 107-118.
50. Brinker K. Incorporating diversity in active learning with support vector machines. In: Proceedings of the International Conference on Machine Learning (ICML). Washington, DC: AAAI Press; 2003, 59-66.