Active learning with multi-label SVM classification

IJCAI International Joint Conference on Artificial Intelligence

Volumn , Issue , 2013, Pages 1479-1485

  Li, Xin ^a   Guo, Yuhong ^a  

^a Temple University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACTIVE LEARNING; ACTIVE LEARNING STRATEGIES; ADAPTIVE FRAMEWORK; INSTANCE SELECTION; MULTI-LABEL CLASSIFICATIONS; MULTI-LABEL INSTANCES; PREDICTION UNCERTAINTY; SVM CLASSIFICATION;

ARTIFICIAL INTELLIGENCE;

EID: 84896062998     PISSN: 10450823     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (32)

1. M. Boutell, J. Luo, X. Shen, and C. Brown. Learning multi-label scene classification. Pattern Recognition, 37(9):1757-1771, 2004.
2. K. Brinker. On active learning in multi-label classification. In "From Data and Information Analysis to Knowledge Engineering" of BookSeries "Studies in Classification, Data Analysis, and Knowledge Organization", Springer, 2006.
3. C. Campbell, N. Cristianini, and A. Smola. Query learning with large margin classifiers. In Proc. of ICML, 2000.
4. K. Crammer and Y. Singer. A family of additive online algorithms for category ranking. JMLR, 3:1025-1058, 2003.
5. A. Culotta and A. McCallum. Reducing labeling effort for structured prediction tasks. In Proc. of AAAI, 2005.
6. Pinar Donmez, Jaime G. Carbonell, and Paul N. Bennett. Dual strategy active learning. In Proc. of ECML, 2007.
7. P. Duygulu, K. Barnard, J de Freitas, and D. Forsyth. Object recognition as machine translation: Learning a lexicon for a fixed image vocabulary. In Proc. of ECCV, 2002.
8. A. Esuli and F. Sebastiani. Active learning strategies for multi-label text classification. In Proc. of ECIR, 2009.
9. Y. Guo and R. Greiner. Optimistic active learning using mutual information. In Proc. of IJCAI, 2007.
10. Y. Guo and D. Schuurmans. Discriminative batch mode active learning. In Proc. of NIPS, 2007.
11. Y. Guo and D. Schuurmans. Adaptive large margin training for multilabel classification. In Proc. of AAAI, 2011.
12. M. Huiskes and M. Lew. The MIR flickr retrieval evaluation. In Proc. of ACM international conference on Multimedia information retrieval, 2008.
13. T. Joachims. Text categorization with suport vector machines: Learning with many relevant features. In Proc. of ECML, 1998.
14. D. Lewis and W. Gale. A sequential algorithm for training text classifiers. In Proc. of Annual Inter. ACM SIGIR Conference on Research and Development in Information Retrieval, 1994.
15. D. Lewis, Y. Yang, T. Rose, and F. Li. RCV1: A new benchmark collection for text categorization research. JMLR, 5:361-397, 2004.
16. D. Lewis, Y. Yang, T. Rose, and F. Li. RCV1: A new benchmark collection for text categorization research. JMLR, 5:361-397, 2004.
17. X. Li, L. Wang, and E. Sung. Multilabel SVM active learning for image classification. In Proc. of ICIP, 2004.
18. D. Lowe. Distinctive image features from scale-invariant keypoints. IJCV, 60(2):91-110, 2004.
19. T. Luo, K. Kramer, D. B. Goldgof, L. O. Hall, S. Samson, A. Remsen, and T. Hopkins. Active learning to recognize multiple types of plankton. JMLR, 6:589-613, 2005.
20. Aude Oliva and Antonio Torralba. Modeling the shape of the scene: A holistic representation of the spatial envelope. IJCV, 42(3):145-175, 2001.
21. G. Qi, X. Hua, Y. Rui, J. Tang, and H. Zhang. Two-dimensional multilabel active learning with an efficient online adaptation model for image classification. TPAMI, 31(10):1880 -1897, 2009.
22. R. Rifkin and A. Klautau. In defense of one-vs-all classification. JMLR, 5:101-141, 2004.
23. N. Roy and A. McCallum. Toward optimal active learning through sampling estimation of error reduction. In Proc. of ICML, 2001.
24. B. Settles and M. Craven. An analysis of active learning strategies for sequence labeling tasks. In Proc. of EMNLP, 2008.
25. B. Settles. Active Learning. Morgan & Claypool, 2012.
26. J. Shotton, J. Winn, C. Rother, and A. Criminisi. Textonboost: joint appearance, shape and context modeling for multi-class object recognition and segmentation. In Proc. of ECCV, 2006.
27. M. Singh, E. Curran, and P. Cunningham. Active learning for multi-label image annotation. Technical report, University College Dublin, 2009.
28. G. Tsoumakas and I. Katakis. Multi-label classification: An overview. Inter. J. of Data Warehousing & Mining, 3(3):1-13, 2007.
29. S. Vijayanarasimhan and K. Grauman. What's it going to cost you?: Predicting effort vs. informativeness for multi-label image annotations. In Proc. of CVPR, 2009.
30. R. Yan, J. Yang, and A. Hauptmann. Automatically labeling video data using multi-class active learning. In Proc. of ICCV, 2003.
31. B. Yang, J. Sun, T. Wang, and Z. Chen. Effective multi-label active learning for text classification. In Proc. of ACM SIGKDD Inter. Conference on Knowledge Discovery and Data Mining, 2009.
32. X. Zhu, J. Lafferty, and Z. Ghahramani. Combining active learning and semi-supervised learning using gaussian fields and harmonic functions. In ICML Workshop on The Continuum from Labeled to Unlabeled Data in Machine Learning and Data Mining, 2003.