메뉴 건너뛰기




Volumn , Issue , 2007, Pages 126-131

Active selection of training examples for meta-learning

Author keywords

[No Author keywords available]

Indexed keywords

EDUCATION; INTELLIGENT CONTROL; INTELLIGENT SYSTEMS; LEARNING SYSTEMS;

EID: 47149108312     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICHIS.2007.4344039     Document Type: Conference Paper
Times cited : (9)

References (23)
  • 3
    • 0037361994 scopus 로고    scopus 로고
    • Ranking learning algorithms: Using IBL and meta-learning on accuracy and time results
    • P. Brazdil, C. Soares, and J. da Costa. Ranking learning algorithms: Using IBL and meta-learning on accuracy and time results. Machine Learning, 50(3):251-277, 2003.
    • (2003) Machine Learning , vol.50 , Issue.3 , pp. 251-277
    • Brazdil, P.1    Soares, C.2    da Costa, J.3
  • 5
    • 0028424239 scopus 로고
    • Improving generalization with active learning
    • D. Cohn, L. Atlas, and R. Ladner. Improving generalization with active learning. Machine Learning, 15:201-221, 1994.
    • (1994) Machine Learning , vol.15 , pp. 201-221
    • Cohn, D.1    Atlas, L.2    Ladner, R.3
  • 6
  • 7
    • 1642379397 scopus 로고    scopus 로고
    • Introduction to the special issue on meta-learning
    • C. Giraud-Carrier, R. Vilalta, and P. Brazdil. Introduction to the special issue on meta-learning. Machine Learning, 54(3): 187-193, 2004.
    • (2004) Machine Learning , vol.54 , Issue.3 , pp. 187-193
    • Giraud-Carrier, C.1    Vilalta, R.2    Brazdil, P.3
  • 9
    • 1642280141 scopus 로고    scopus 로고
    • On data and algorithms - understanding inductive performance
    • A. Kalousis, J. Gama, and M. Hilario. On data and algorithms - understanding inductive performance. Machine Learning, 54(3):275-312, 2004.
    • (2004) Machine Learning , vol.54 , Issue.3 , pp. 275-312
    • Kalousis, A.1    Gama, J.2    Hilario, M.3
  • 10
    • 0000153354 scopus 로고    scopus 로고
    • Noemon: Design, implementation and performance results of an intelligent assistant for classifier selection
    • A. Kalousis and T. Theoharis. Noemon: Design, implementation and performance results of an intelligent assistant for classifier selection. Intelligent Data Analysis, 3(5):319-337, 1999.
    • (1999) Intelligent Data Analysis , vol.3 , Issue.5 , pp. 319-337
    • Kalousis, A.1    Theoharis, T.2
  • 11
    • 0039870976 scopus 로고
    • A time-delay neural network architecture for speech recognition
    • Technical Report CMU-DS-88-152, Dept. of Computer Science, Carnegie Mellon University, Pittsburgh, PA, Dec
    • K. J. Lang and G. E. Hinton. A time-delay neural network architecture for speech recognition. Technical Report CMU-DS-88-152, Dept. of Computer Science, Carnegie Mellon University, Pittsburgh, PA, Dec. 1988.
    • (1988)
    • Lang, K.J.1    Hinton, G.E.2
  • 14
    • 1242352526 scopus 로고    scopus 로고
    • Selective sampling for nearest neighbor classifiers
    • M. Lindenbaum, S. Markovitch, and D. Rusakov. Selective sampling for nearest neighbor classifiers. Machine Learning, 54:125-152, 2004.
    • (2004) Machine Learning , vol.54 , pp. 125-152
    • Lindenbaum, M.1    Markovitch, S.2    Rusakov, D.3
  • 15
    • 47149089419 scopus 로고    scopus 로고
    • R. B. C. Prudéncio and T. B. Ludermir. Selection of models for time series prediction via meta-learning. In Proceedings of the Second International Conference on Hybrid Systems, pages 74-83. IOS Press, 2002.
    • R. B. C. Prudéncio and T. B. Ludermir. Selection of models for time series prediction via meta-learning. In Proceedings of the Second International Conference on Hybrid Systems, pages 74-83. IOS Press, 2002.
  • 16
    • 10244243684 scopus 로고    scopus 로고
    • Meta-learning approaches to selecting time series models
    • R. B. C. Prudêncio and T. B. Ludermir. Meta-learning approaches to selecting time series models. Neurocomputing, 61:121-137, 2004.
    • (2004) Neurocomputing , vol.61 , pp. 121-137
    • Prudêncio, R.B.C.1    Ludermir, T.B.2
  • 17
    • 47149091216 scopus 로고    scopus 로고
    • R. B. C. Prudêncio and T. B. Ludermir. Active learning to support the generation of meta-examples. In Proc. of the International Conference on Artificial Neural Networks, page (to appear), 2007.
    • R. B. C. Prudêncio and T. B. Ludermir. Active learning to support the generation of meta-examples. In Proc. of the International Conference on Artificial Neural Networks, page (to appear), 2007.
  • 19
    • 0442319140 scopus 로고    scopus 로고
    • Toward optimal active learning through sampling estimation of error reduction
    • Morgan Kaufmann, San Francisco, CA
    • N. Roy and A. McCallum. Toward optimal active learning through sampling estimation of error reduction. In Proc. 18th International Conf. on Machine Learning, pages 441-448. Morgan Kaufmann, San Francisco, CA, 2001.
    • (2001) Proc. 18th International Conf. on Machine Learning , pp. 441-448
    • Roy, N.1    McCallum, A.2
  • 22
    • 0042868698 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. Journal of Machine Learning Research, 2:45-66, 2002.
    • (2002) Journal of Machine Learning Research , vol.2 , pp. 45-66
    • Tong, S.1    Koller, D.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.