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Volumn 2015-January, Issue , 2015, Pages 2962-2970

Efficient and robust automated machine learning

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; INFORMATION SCIENCE;

EID: 84965128050     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (1602)

References (28)
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    • Thornton, C.1    Hutter, F.2    Hoos, H.3    Leyton-Brown, K.4
  • 4
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    • Initializing Bayesian hyperparameter optimization via metalearning
    • M. Feurer, J. Springenberg, and F. Hutter. Initializing Bayesian hyperparameter optimization via metalearning. In Proc. of AAAI'15, pages 1128-1135, 2015.
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    • Feurer, M.1    Springenberg, J.2    Hutter, F.3
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    • Meta-learning for evolutionary parameter optimization of classifiers
    • Reif M, F. Shafait, and A. Dengel. Meta-learning for evolutionary parameter optimization of classifiers. Machine Learning, 87:357-380, 2012.
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    • Reif, M.1    Shafait, F.2    Dengel, A.3
  • 6
    • 82455210873 scopus 로고    scopus 로고
    • Combining meta-learning and search techniques to select parameters for support vector machines
    • T. Gomes, R. Prudêncio, C. Soares, A. Rossi, and A. Carvalho. Combining meta-learning and search techniques to select parameters for support vector machines. Neurocomputing, 75(1):3-13, 2012.
    • (2012) Neurocomputing , vol.75 , Issue.1 , pp. 3-13
    • Gomes, T.1    Prudêncio, R.2    Soares, C.3    Rossi, A.4    Carvalho, A.5
  • 9
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    • Sequential model-based optimization for general algorithm configuration
    • F. Hutter, H. Hoos, and K. Leyton-Brown. Sequential model-based optimization for general algorithm configuration. In Proc. of LION'11, pages 507-523, 2011.
    • (2011) Proc. of LION'11 , pp. 507-523
    • Hutter, F.1    Hoos, H.2    Leyton-Brown, K.3
  • 11
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    • Practical Bayesian optimization of machine learning algorithms
    • J. Snoek, H. Larochelle, and R. P. Adams. Practical Bayesian optimization of machine learning algorithms. In Proc. of NIPS'12, pages 2960-2968, 2012.
    • (2012) Proc. of NIPS'12 , pp. 2960-2968
    • Snoek, J.1    Larochelle, H.2    Adams, R.P.3
  • 13
    • 84908279482 scopus 로고    scopus 로고
    • Hyperopt-sklearn: Automatic hyperparameter configuration for scikit-learn
    • B. Komer, J. Bergstra, and C. Eliasmith. Hyperopt-sklearn: Automatic hyperparameter configuration for scikit-learn. In ICML workshop on AutoML, 2014.
    • (2014) ICML Workshop on AutoML
    • Komer, B.1    Bergstra, J.2    Eliasmith, C.3
  • 14
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    • Random forests
    • L. Breiman. Random forests. MLJ, 45:5-32, 2001.
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    • Efficient transfer learning method for automatic hyperparameter tuning
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    • Model selection: Beyond the Bayesian/Frequentist divide
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.