메뉴 건너뛰기




Volumn 43, Issue 2, 2013, Pages 464-475

Active learning with optimal instance subset selection

Author keywords

Active learning; Instance subset selection; Machine learning

Indexed keywords

ACTIVE LEARNING; OPTIMAL SUBSETS; SEMIDEFINITE PROGRAMMING PROBLEM; STATE-OF-THE-ART APPROACH; SUBSET SELECTION; UTILITY MEASURE; UTILITY VALUES;

EID: 84890425291     PISSN: 21682267     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSMCB.2012.2209177     Document Type: Article
Times cited : (18)

References (33)
  • 2
    • 0002332781 scopus 로고    scopus 로고
    • Employing EM in pool-based active learning for text classification
    • A. McCallum and K. Nigam, "Employing EM in pool-based active learning for text classification," in Proc. ICML, 1998, pp. 359-367.
    • Proc. ICML, 1998 , pp. 359-367
    • McCallum, A.1    Nigam, K.2
  • 3
    • 0003007938 scopus 로고    scopus 로고
    • Support vector machine active learning with applications to text classification
    • S. Tong and D. Koller, "Support vector machine active learning with applications to text classification," in Proc. ICML, 2000, pp. 999-1006.
    • Proc. ICML, 2000 , pp. 999-1006
    • Tong, S.1    Koller, D.2
  • 5
    • 75149177725 scopus 로고    scopus 로고
    • Active machine learning for transmembrane helix prediction
    • Jan.
    • H. Osmanbeyoglu, J. Wehner, J. Carbonell, and M. Ganapathiraju, "Active machine learning for transmembrane helix prediction," J. BMC Bioinformat., vol. 11, no. S58, pp. 1-9, Jan. 2010.
    • (2010) J. BMC Bioinformat. , vol.11 , Issue.S58 , pp. 1-9
    • Osmanbeyoglu, H.1    Wehner, J.2    Carbonell, J.3    Ganapathiraju, M.4
  • 6
    • 78649975675 scopus 로고    scopus 로고
    • Active learning from stream data using optimal weight classifier ensemble
    • Dec.
    • X. Zhu, P. Zhang, X. Lin, and Y. Shi, "Active learning from stream data using optimal weight classifier ensemble," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 40, no. 6, pp. 1607-1621, Dec. 2010.
    • (2010) IEEE Trans. Syst., Man, Cybern. B, Cybern. , vol.40 , Issue.6 , pp. 1607-1621
    • Zhu, X.1    Zhang, P.2    Lin, X.3    Shi, Y.4
  • 7
    • 79959582478 scopus 로고    scopus 로고
    • Active learning for solving the incomplete data problem in facial age classification by the furthest nearest neighbor criterion
    • Jul.
    • J.Wang, E. Sung, andW. Yau, "Active learning for solving the incomplete data problem in facial age classification by the furthest nearest neighbor criterion," IEEE Trans. Image Process., vol. 20, no. 7, pp. 2049-2062, Jul. 2011.
    • (2011) IEEE Trans. Image Process. , vol.20 , Issue.7 , pp. 2049-2062
    • Wang, J.1    Sung, E.2    Yau, W.3
  • 8
    • 1942517333 scopus 로고    scopus 로고
    • Incorporating diversity in active learning with support vector machines
    • K. Brinker, "Incorporating diversity in active learning with support vector machines," in Proc. ICML, 2003, pp. 59-66.
    • Proc. ICML, 2003 , pp. 59-66
    • Brinker, K.1
  • 9
    • 37149038648 scopus 로고    scopus 로고
    • Incorporating diversity and density in active learning for relevance feedback
    • Z. Xu, R. Akella, and Y. Zhang, "Incorporating diversity and density in active learning for relevance feedback," in Proc. ECIR, 2007, pp. 246-257.
    • Proc. ECIR, 2007 , pp. 246-257
    • Xu, Z.1    Akella, R.2    Zhang, Y.3
  • 10
    • 34250745927 scopus 로고    scopus 로고
    • Batch mode active learning and its application to medical image classification
    • S. C. H. Hoi, R. Jin, J. Zhu, and M. R. Lyu, "Batch mode active learning and its application to medical image classification," in Proc. ICML, 2006, pp. 417-424.
    • Proc. ICML, 2006 , pp. 417-424
    • Hoi, S.C.H.1    Jin, R.2    Zhu, J.3    Lyu, M.R.4
  • 11
    • 79551702937 scopus 로고    scopus 로고
    • Discriminative batch mode active learning
    • Y. Guo and D. Schuurmans, "Discriminative batch mode active learning," in Proc. NIPS, 2007, pp. 1-8.
    • Proc. NIPS, 2007 , pp. 1-8
    • Guo, Y.1    Schuurmans, D.2
  • 12
    • 0001025146 scopus 로고    scopus 로고
    • Query learning strategies using boosting and bagging
    • H. Abe and H. Mamitsuka, "Query learning strategies using boosting and bagging," in Proc. ICML, 1998, pp. 1-9.
    • Proc. ICML, 1998 , pp. 1-9
    • Abe, H.1    Mamitsuka, H.2
  • 13
    • 80053375448 scopus 로고    scopus 로고
    • An analysis of active learning strategies for sequence labeling tasks
    • B. Settles and M. Craven, "An analysis of active learning strategies for sequence labeling tasks," in Proc. Conf. Emp. Methods NLP, 2008, pp. 1-10.
    • Proc. Conf. Emp. Methods NLP, 2008 , pp. 1-10
    • Settles, B.1    Craven, M.2
  • 14
    • 29344432464 scopus 로고    scopus 로고
    • Reducing labeling effort for structured prediction tasks
    • A. Culotta and A. McCallum, "Reducing labeling effort for structured prediction tasks," in Proc. AAAI, 2005, pp. 746-751.
    • Proc. AAAI, 2005 , pp. 746-751
    • Culotta, A.1    McCallum, A.2
  • 15
    • 84880855398 scopus 로고    scopus 로고
    • Optimistic active learning using mutual information
    • Y. Guo and R. Greiner, "Optimistic active learning using mutual information," in Proc. IJCAI, 2007, pp. 823-829.
    • Proc. IJCAI, 2007 , pp. 823-829
    • Guo, Y.1    Greiner, R.2
  • 16
    • 0442319140 scopus 로고    scopus 로고
    • Toward optimal active learning through sampling estimation of error reduction
    • N. Roy and A. McCallum, "Toward optimal active learning through sampling estimation of error reduction," in Proc. ICML, 2001, pp. 441-448.
    • Proc. ICML, 2001 , pp. 441-448
    • Roy, N.1    McCallum, A.2
  • 17
    • 79953146263 scopus 로고    scopus 로고
    • A general framework for measuring inconsistency through minimal inconsistent sets
    • Apr.
    • K. Mu, W. Liu, and Z. Jin, "A general framework for measuring inconsistency through minimal inconsistent sets," Knowl. Inf. Syst., vol. 27, no. 1, pp. 85-114, Apr. 2011.
    • (2011) Knowl. Inf. Syst. , vol.27 , Issue.1 , pp. 85-114
    • Mu, K.1    Liu, W.2    Jin, Z.3
  • 18
    • 84893574327 scopus 로고
    • Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming
    • Nov.
    • M. Goemans and D. Williamson, "Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming," J. ACM, vol. 42, no. 6, pp. 1115-1145, Nov. 1995.
    • (1995) J. ACM , vol.42 , Issue.6 , pp. 1115-1145
    • Goemans, M.1    Williamson, D.2
  • 19
    • 33745794076 scopus 로고    scopus 로고
    • Ensemble pruning via semi-definite programming
    • Dec.
    • Y. Zhang, S. Burer, and W. Street, "Ensemble pruning via semi-definite programming," J. Mach. Learn. Res., vol. 57, pp. 1315-1338, Dec. 2006.
    • (2006) J. Mach. Learn. Res. , vol.57 , pp. 1315-1338
    • Zhang, Y.1    Burer, S.2    Street, W.3
  • 22
    • 79959494853 scopus 로고    scopus 로고
    • Improving SVM classification on imbalanced time series data sets with ghost points
    • Jul.
    • S. Kknar-Tezel and L. Latecki, "Improving SVM classification on imbalanced time series data sets with ghost points," Knowl. Inf. Syst., vol. 28, no. 1, pp. 1-23, Jul. 2011.
    • (2011) Knowl. Inf. Syst. , vol.28 , Issue.1 , pp. 1-23
    • Kknar-Tezel, S.1    Latecki, L.2
  • 26
    • 77951197218 scopus 로고    scopus 로고
    • Two heads better than one: Metric +active learning and its applications for IT service classification
    • F. Wang, J. Sun, T. Li, and N. Anerousis, "Two heads better than one: Metric +active learning and its applications for IT service classification," in Proc. IEEE ICDM, Miami, FL, 2009, pp. 1022-1027.
    • Proc. IEEE ICDM, Miami, FL, 2009 , pp. 1022-1027
    • Wang, F.1    Sun, J.2    Li, T.3    Anerousis, N.4
  • 27
    • 15344349101 scopus 로고    scopus 로고
    • Active learning using pre-clustering
    • H. Nguyen and A. Smeulders, "Active learning using pre-clustering," in Proc. ICML, 2004, pp. 79-86.
    • Proc. ICML, 2004 , pp. 79-86
    • Nguyen, H.1    Smeulders, A.2
  • 28
    • 0018306059 scopus 로고
    • A threshold selection method from gray-level histogram
    • Jan.
    • N. Otsu, "A threshold selection method from gray-level histogram," IEEE Trans. Syst., Man, Cybern., vol. SMC-9, no. 1, pp. 62-66, Jan. 1979.
    • (1979) IEEE Trans. Syst., Man, Cybern. , vol.9 , Issue.1 , pp. 62-66
    • Otsu, N.1
  • 30
    • 79961211866 scopus 로고    scopus 로고
    • An overview on subgroup discovery: Foundations and applications
    • Dec.
    • F. Herrera, C. Carmona, P. Gonzalez, and M. Jesus, "An overview on subgroup discovery: Foundations and applications," Knowl. Inf. Syst., vol. 29, no. 3, pp. 495-525, Dec. 2011.
    • (2011) Knowl. Inf. Syst. , vol.29 , Issue.3 , pp. 495-525
    • Herrera, F.1    Carmona, C.2    Gonzalez, P.3    Jesus, M.4
  • 31
    • 70349254685 scopus 로고    scopus 로고
    • Proactive learning: Cost-sensitive active learning with multiple imperfect oracles
    • P. Donmez and J. Carbonell, "Proactive learning: Cost-sensitive active learning with multiple imperfect oracles," in Proc. CIKM, 2008, pp. 619-628.
    • Proc. CIKM, 2008 , pp. 619-628
    • Donmez, P.1    Carbonell, J.2
  • 32
    • 84876057388 scopus 로고    scopus 로고
    • A survey on instance selection for active learning
    • doi:10.1007/s10115-012-0507-8, to be published
    • Y. Fu, X. Zhu, and B. Li, "A survey on instance selection for active learning," Knowl. Inf. Syst., 2012. doi:10.1007/s10115-012-0507-8, to be published.
    • (2012) Knowl. Inf. Syst.
    • Fu, Y.1    Zhu, X.2    Li, B.3
  • 33
    • 0042486152 scopus 로고    scopus 로고
    • Implementation and evaluation of SDPA 6.0
    • M. Yamashita, K. Fujisawa, and M. Kojima, "Implementation and evaluation of SDPA 6.0," Optim. Methods Softw., vol. 18, no. 4, pp. 491-505, 2003.
    • (2003) Optim. Methods Softw. , vol.18 , Issue.4 , pp. 491-505
    • Yamashita, M.1    Fujisawa, K.2    Kojima, M.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.