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Volumn , Issue , 2013, Pages 18-25

Quantitative Measurements of model interpretability for the analysis of spectral data

Author keywords

[No Author keywords available]

Indexed keywords

EXTRACT INFORMATIONS; INTERPRETABILITY; MACHINE LEARNING METHODS; MODEL PARAMETERS; MODEL SIZE; QUANTITATIVE MEASUREMENT; QUANTITATIVE MEASURES; SPECTRAL DATA;

EID: 84885634946     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CIDM.2013.6597212     Document Type: Conference Paper
Times cited : (7)

References (11)
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    • High-throughput quality control of coffee varieties and blends by artificial neural networks from hyperspectral imaging
    • F. Travaglia, M. Bordiga, J. Co?sson, M. Locatelli, V. Fogliano, and M. Arlorio, Eds Novara, Italy
    • A. Backhaus, F. Bollenbeck, and U. Seiffert, "High-throughput quality control of coffee varieties and blends by artificial neural networks from hyperspectral imaging," in Proceedings of the 1st International Congress on Cocoa, Coffee and Tea (CoCoTea), F. Travaglia, M. Bordiga, J. Co?sson, M. Locatelli, V. Fogliano, and M. Arlorio, Eds., vol. 1, Novara, Italy, 2011, pp. 88-92.
    • (2011) Proceedings of the 1st International Congress on Cocoa, Coffee and Tea (CoCoTea) , vol.1 , pp. 88-92
    • Backhaus, A.1    Bollenbeck, F.2    Seiffert, U.3
  • 4
    • 80755180926 scopus 로고    scopus 로고
    • Near infrared spectroscopic analysis of single malt scotch whisky on an optofluidic chip
    • Nov
    • P. C. Ashok, B. B. Praveen, and K. Dholakia, "Near infrared spectroscopic analysis of single malt scotch whisky on an optofluidic chip." Opt Express, vol. 19, no. 23, pp. 22 982-22 992, Nov 2011.
    • (2011) Opt Express , vol.19 , Issue.23 , pp. 22982-22992
    • Ashok, P.C.1    Praveen, B.B.2    Dholakia, K.3
  • 5
    • 0000672424 scopus 로고
    • Fast learning in networks of locally tuned processing units
    • J. Moody and C. J. Darken, "Fast learning in networks of locally tuned processing units," Neural Computation, vol. 1, pp. 281-294, 1989.
    • (1989) Neural Computation , vol.1 , pp. 281-294
    • Moody, J.1    Darken, C.J.2
  • 7
    • 0036791938 scopus 로고    scopus 로고
    • Generalized relevance learning vector quantization
    • B. Hammer and T. Villmann, "Generalized relevance learning vector quantization," Neural Networks, vol. 15, pp. 1059-1068, 2002.
    • (2002) Neural Networks , vol.15 , pp. 1059-1068
    • Hammer, B.1    Villmann, T.2
  • 8
    • 12844250052 scopus 로고    scopus 로고
    • Supervised neural gas with general similarity measure
    • B. Hammer, M. Strickert, and T. Villmann, "Supervised Neural Gas with general similarity measure," Neural Processing Letters, vol. 21, pp. 21-44, 2005.
    • (2005) Neural Processing Letters , vol.21 , pp. 21-44
    • Hammer, B.1    Strickert, M.2    Villmann, T.3
  • 10
    • 0000742931 scopus 로고
    • A neural-gas network learns topologies
    • T. Kohonen, K. Makisara, O. Simula, and J. Kangas, Eds. North-Holland, Amsterdam
    • T. M. Martinetz and K. J. Schulten, "A Neural-Gas network learns topologies," in Artificial Neural Networks, T. Kohonen, K. Makisara, O. Simula, and J. Kangas, Eds. North-Holland, Amsterdam, 1991, pp. 397-402.
    • (1991) Artificial Neural Networks , pp. 397-402
    • Martinetz, T.M.1    Schulten, K.J.2
  • 11
    • 24344458137 scopus 로고    scopus 로고
    • Feature selection based on mutual information: Criteria of max-dependency, max-relevance, and minredundancy
    • H. Peng, F. Long, and C. Ding, "Feature selection based on mutual information: criteria of max-dependency, max-relevance, and minredundancy," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, pp. 1226-1238, 2005.
    • (2005) IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.27 , pp. 1226-1238
    • Peng, H.1    Long, F.2    Ding, C.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.