Publications

My pages on Google Scholar, dblp and on ResearchGate

  • H. Cao, S. Bernard, R. Sabourin, and L. Heutte, « Random forest dissimilarity based multi-view learning for radiomics applications » Pattern Recognition, vol. 88, p. 185–197, 2019.
  • H. Cao, S. Bernard, L. Heutte, and R. Sabourin, « Dynamic voting in multi-view learning for radiomics applications » in Joint IAPR international workshop on structural, syntactic, and statistical pattern recognition (s+sspr), 2018, p. 32–41.
  • H. Cao, S. Bernard, L. Heutte, and R. Sabourin, « Improve the performance of transfer learning without fine-tuning using dissimilarity-based multi-view learning for breast cancer histology images » in International conference image analysis and recognition (iciar), 2018, p. 779–787.
  • H. Cao, S. Bernard, L. Heutte, and R. Sabourin, « Pondération dynamique en apprentissage multi-vues pour des applications radiomics » in Conférence sur l’apprentissage automatique (cap), 2018.
  • H. Cao, S. Bernard, L. Heutte, and R. Sabourin, « Dissimilarity-based representation for radiomics applications » in International conference on pattern recognition and artificial intelligence (icprai), 2018.
  • S. Bernard, C. Chatelain, S. Adam, and R. Sabourin, « The multiclass roc front method for cost-sensitive classification » Pattern Recognition, vol. 52, p. 46–60, 2016.
  • C. Dubos, S. Bernard, S. Adam, and R. Sabourin, « ROC-based cost-sensitive classification with a reject option » in International conference on pattern recognition (icpr), 2016, p. 3320–3325.
  • S. Bernard, C. Chatelain, S. Adam, and R. Sabourin, « Apprentissage multiclasse en environnement incertain » in 21ème rencontre de la société francophone de classification (sfc), 2014.
  • C. Désir, S. Bernard, C. Petitjean, and L. Heutte, « One class random forests » Pattern Recognition, vol. 46, iss. 12, p. 3490–3506, 2013.
  • S. Bernard, S. Adam, and L. Heutte, « Dynamic random forests » Pattern Recognition Letters, vol. 33, iss. 12, p. 1580–1586, 2012.
  • E. Vintrou, M. Soumaré, S. Bernard, A. Bégué, C. Baron, and D. Lo Seen, « Mapping fragmented agricultural systems in the sudano-sahelian environments of africa using random forest and ensemble metrics of coarse resolution modis imagery » Photogrammetric engineering & remote sensing, vol. 78, iss. 8, p. 839–848, 2012.
  • C. Désir, S. Bernard, C. Petitjean, and L. Heutte, « A new random forest method for one-class classification » in Joint IAPR international workshops on statistical techniques in pattern recognition (spr) and structural and syntactic pattern recognition (sspr), 2012, p. 282–290.
  • C. Désir, S. Bernard, C. Petitjean, and L. Heutte, « A random forest based approach for one class classification in medical imaging » in International workshop on machine learning in medical imaging (mlmi), 2012, p. 250–257.
  • S. Bernard, L. Heutte, and S. Adam, « A study of strength and correlation in random forests » in International conference on intelligent computing (icic), 2010, p. 186–191.
  • S. Bernard, « Forêts aléatoires: de l’analyse des mécanismes de fonctionnement à la construction dynamique » PhD Thesis, 2009.
  • S. Bernard, L. Heutte, and S. Adam, « On the selection of decision trees in random forests » in International joint conference on neural networks (ijcnn), 2009, p. 302–307.
  • S. Bernard, L. Heutte, and S. Adam, « Influence of hyperparameters on random forest accuracy » in International workshop on multiple classifier systems (mcs), 2009, p. 171–180.
  • S. Bernard, L. Heutte, and S. Adam, « Towards a better understanding of random forests through the study of strength and correlation » in International conference on intelligent computing (icic), 2009, p. 536–545.
  • S. Bernard, L. Heutte, and S. Adam, « Une étude sur la paramétrisation des forêts aléatoires » in Conférence sur l’apprentissage automatique (cap), 2009, p. 81–92.
  • S. Bernard, L. Heutte, and S. Adam, « Forest-rk: a new random forest induction method » in International conference on intelligent computing (icic), 2008, p. 430–437.
  • S. Bernard, L. Heutte, and S. Adam, « Etude de l’influence des paramètres sur les performances des forêts aléatoires » in Colloque international francophone sur l’ecrit et le document (cifed), 2008, p. 207–208.
  • S. Bernard, S. Adam, and L. Heutte, « Using random forests for handwritten digit recognition » in International conference on document analysis and recognition (icdar), 2007, p. 1043–1047.