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Volumn , Issue , 2005, Pages

Active learning for anomaly and rare-category detection

Author keywords

[No Author keywords available]

Indexed keywords

ACTIVE LEARNING; CLASS LABELS; DATAPOINTS; EMPIRICAL ANALYSIS; HUMAN EXPERT; MIXTURE COMPONENTS; MIXTURE MODEL;

EID: 84899016906     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (45)

References (20)
  • 4
    • 85153947869 scopus 로고
    • Active learning with statistical models
    • G. Tesauro, D. Touretzky, and T. Leen, editors, The MIT Press
    • David A. Cohn, Zoubin Ghahramani, and Michael I. Jordan. Active learning with statistical models. In G. Tesauro, D. Touretzky, and T. Leen, editors, Advances in Neural Information Processing Systems, volume 7, pages 705-712. The MIT Press, 1995.
    • (1995) Advances in Neural Information Processing Systems , vol.7 , pp. 705-712
    • Cohn, D.A.1    Ghahramani, Z.2    Jordan, M.I.3
  • 6
    • 0028424239 scopus 로고
    • Improving generalization with active learning
    • David Cohn, Les Atlas, and Richard Ladner. Improving generalization with active learning. Machine Learning, 15(2):201-221, 1994.
    • (1994) Machine Learning , vol.15 , Issue.2 , pp. 201-221
    • Cohn, D.1    Atlas, L.2    Ladner, R.3
  • 7
    • 0027560678 scopus 로고
    • Selecting concise training sets from clean data
    • March
    • Mark Plutowski and Halbert White. Selecting concise training sets from clean data. IEEE Transactions on Neural Networks, 4(2):305-318, March 1993.
    • (1993) IEEE Transactions on Neural Networks , vol.4 , Issue.2 , pp. 305-318
    • Plutowski, M.1    White, H.2
  • 8
    • 0028499630 scopus 로고
    • The effect of unlabeled examples in reducing the small sample size problem
    • Shahshashani and Landgrebe. The effect of unlabeled examples in reducing the small sample size problem. IEEE Trans Geoscience and Remote Sensing, 32(5):1087-1095, 1994.
    • (1994) IEEE Trans Geoscience and Remote Sensing , vol.32 , Issue.5 , pp. 1087-1095
    • Shahshashani1    Landgrebe2
  • 9
    • 84898980291 scopus 로고    scopus 로고
    • A mixture of experts classifier with learning based on both labeled and unlabelled data
    • Miller and Uyar. A mixture of experts classifier with learning based on both labeled and unlabelled data. In NIPS-9, 1997.
    • (1997) NIPS-9
    • Miller1    Uyar2
  • 11
    • 85124125604 scopus 로고
    • Heterogeneous uncertainty sampling for supervised learning
    • William W. Cohen and Haym Hirsh, editors, New Brunswick, US, Morgan Kaufmann Publishers, San Francisco, US
    • David D. Lewis and Jason Catlett. Heterogeneous uncertainty sampling for supervised learning. In William W. Cohen and Haym Hirsh, editors, Proceedings of ICML-94, 11th International Conference on Machine Learning, pages 148-156, New Brunswick, US, 1994. Morgan Kaufmann Publishers, San Francisco, US.
    • (1994) th International Conference on Machine Learning , pp. 148-156
    • Lewis, D.D.1    Catlett, J.2
  • 14
  • 16
    • 36849033557 scopus 로고    scopus 로고
    • Scalable and practical probability density estimators for scientific anomaly detection
    • PhD thesis
    • Dan Pelleg. Scalable and Practical Probability Density Estimators for Scientific Anomaly Detection. PhD thesis, Carnegie-Mellon University, 2004. Tech Report CMU-CS-04-134.
    • (2004) Carnegie-Mellon University Tech Report CMU-CS-04-134
    • Pelleg, D.1
  • 17
    • 0000695404 scopus 로고
    • Information-based objective functions for active data selection
    • David MacKay. Information-based objective functions for active data selection. Neural Computation, 4(4):590-604, 1992.
    • (1992) Neural Computation , vol.4 , Issue.4 , pp. 590-604
    • Mackay, D.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.