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Mondher Maddouri: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Rabie Saidi, Mondher Maddouri, Engelbert Mephu Nguifo
    Classification supervisée de séquences biologiques basée sur les motifs et les matrices de substitution. [Citation Graph (0, 0)][DBLP]
    EGC, 2007, pp:409-420 [Conf]
  2. Mondher Maddouri, Fatma Kaabi
    On Statistical Measures for Selecting Pertinent Formal Concepts to Discover Production Rules from Data. [Citation Graph (0, 0)][DBLP]
    ICDM Workshops, 2006, pp:780-784 [Conf]
  3. Rabie Saidi, Mondher Maddouri, Engelbert Mephu Nguifo
    Biological Sequences Encoding for Supervised Classification. [Citation Graph (0, 0)][DBLP]
    BIRD, 2007, pp:224-238 [Conf]
  4. Mondher Maddouri
    Towards a machine learning approach based on incremental concept formation. [Citation Graph (0, 0)][DBLP]
    Intell. Data Anal., 2004, v:8, n:3, pp:267-280 [Journal]
  5. Mondher Maddouri, Samir Elloumi, Ali Jaoua
    An Incrementa Learning System for Imprecise and Uncertain Knowledge Discovery. [Citation Graph (0, 0)][DBLP]
    Inf. Sci., 1998, v:109, n:1-4, pp:149-164 [Journal]
  6. Mondher Maddouri, Mourad Elloumi
    Encoding of Primary Structures of Biological Macromolecules Within a Data Mining Perspective. [Citation Graph (0, 0)][DBLP]
    J. Comput. Sci. Technol., 2004, v:19, n:1, pp:78-88 [Journal]
  7. Mourad Elloumi, Mondher Maddouri
    New voting strategies designed for the classification of nucleic sequences. [Citation Graph (0, 0)][DBLP]
    Knowl. Inf. Syst., 2005, v:8, n:1, pp:1-15 [Journal]
  8. Mondher Maddouri, Mourad Elloumi
    A data mining approach based on machine learning techniques to classify biological sequences. [Citation Graph (0, 0)][DBLP]
    Knowl.-Based Syst., 2002, v:15, n:4, pp:217-223 [Journal]
  9. Mondher Maddouri, Jamil Gammoudi
    On Semantic Properties of Interestingness Measures for Extracting Rules from Data. [Citation Graph (0, 0)][DBLP]
    ICANNGA (1), 2007, pp:148-158 [Conf]

  10. Improving Boosting by Exploiting Former Assumptions. [Citation Graph (, )][DBLP]

  11. Une nouvelle approche du Boosting face aux données réelles. [Citation Graph (, )][DBLP]

  12. Apprentissage supervisé adaptatif de Concepts Formels à partir des données nominales. [Citation Graph (, )][DBLP]

  13. Générer des règles de classification par dopage de concepts formels. [Citation Graph (, )][DBLP]

  14. Etude de stabilité de méthodes d'extraction de motifs à partir des séquences protéiques. [Citation Graph (, )][DBLP]

  15. Développement de méthodes de classification basées sur l'analyse de concepts formels sous la plateforme WEKA. [Citation Graph (, )][DBLP]

  16. Boosting Formal Concepts to Discover Classification Rules. [Citation Graph (, )][DBLP]

  17. Incremental Rule Production: Towards a Uniform Approach for Knowledge Organization. [Citation Graph (, )][DBLP]

  18. Comparing graph-based representations of protein for mining purposes. [Citation Graph (, )][DBLP]

  19. Protein sequences classification by means of feature extraction with substitution matrices. [Citation Graph (, )][DBLP]

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