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Journal Article

  1. [AZAM-17]“Converting SVDD scores into probability estimates: Application to outlier detection”, Neurocomputing, 2017.
    http://www.sciencedirect.com/science/article/pii/S0925231217307543
  2. [NIAF-14a]E. Niaf, Flamary, R., Rouvière, O., Lartizien, C., and Canu, S., “Kernel-Based Learning From Both Qualitative And Quantitative Labels : Application To Prostate Cancer Diagnosis Based On Multiparametric MR Imaging”, IEEE Transactions on Image Processing, vol. 23, pp. 979- 991, 2014.
    http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6690197&queryText%3Dniaf

Conference Paper

  1. [AZAM-16a]“Converting SVDD scores into probability estimates”, in European Symposium on Articial Neural Networks, Computational Intelligence and Machine Learning (ESANN), 2016.
  2. [AZAM-14]M. El Azami, Lartizien, C., and Canu, S., “Robust outlier detection with L0-SVDD”, in European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Bruges (Belgium), 2014.
  3. [NIAF-12a]E. Niaf, Flamary, R., Canu, S., Rouvière, O., and Lartizien, C., “Handling learning samples uncertainties in SVM classification: application to MRI-based prostate cancer computer-aided diagnosis”, in IEEE International Symposium on Biomedical Imaging (ISBI), Barcelona, Spain, 2012.
  4. [NIAF-11b]E. Niaf, Flamary, R., Lartizien, C., and Canu, S., “Handling uncertainties in SVM classification”, in IEEE Workshop on Statistical Signal Processing, 2011, pp. 757-760.
    http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=5967814&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D5967814