chapitre6.bib

@article{JSSv025i01,
  author = {Sébastien Lê and Julie Josse and François Husson},
  title = {FactoMineR: An R Package for Multivariate Analysis},
  journal = {Journal of Statistical Software, Articles},
  volume = {25},
  number = {1},
  year = {2008},
  keywords = {},
  abstract = {In this article, we present FactoMineR an R package dedicated to multivariate data analysis. The main features of this package is the possibility to take into account different types of variables (quantitative or categorical), different types of structure on the data (a partition on the variables, a hierarchy on the variables, a partition on the individuals) and finally supplementary information (supplementary individuals and variables). Moreover, the dimensions issued from the different exploratory data analyses can be automatically described by quantitative and/or categorical variables. Numerous graphics are also available with various options. Finally, a graphical user interface is implemented within the Rcmdr environment in order to propose an user friendly package.},
  issn = {1548-7660},
  pages = {1--18},
  doi = {10.18637/jss.v025.i01},
  url = {https://www.jstatsoft.org/v025/i01}
}
@inproceedings{Yan:2017:YAY:3109859.3109923,
  author = {Yan, Zhixian and Wei, Lai and Lu, Yunshan and Wu, Zhongqiang and Tao, Bo},
  title = {You Are What Apps You Use: Transfer Learning for Personalized Content and Ad Recommendation},
  booktitle = {Proceedings of the Eleventh ACM Conference on Recommender Systems},
  series = {RecSys '17},
  year = {2017},
  isbn = {978-1-4503-4652-8},
  location = {Como, Italy},
  pages = {350--350},
  numpages = {1},
  url = {http://doi.acm.org/10.1145/3109859.3109923},
  doi = {10.1145/3109859.3109923},
  acmid = {3109923},
  publisher = {ACM},
  address = {New York, NY, USA},
  keywords = {personalized recommendation, transfer learning, user profile}
}
@article{platt1999probabilistic,
  title = {Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods},
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  publisher = {Cambridge, MA}
}
@inproceedings{zadrozny2002transforming,
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  author = {Zadrozny, Bianca and Elkan, Charles},
  booktitle = {Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining},
  pages = {694--699},
  year = {2002},
  organization = {ACM}
}
@inproceedings{schwartz2009human,
  title = {Human detection using partial least squares analysis},
  author = {Schwartz, William Robson and Kembhavi, Aniruddha and Harwood, David and Davis, Larry S},
  booktitle = {2009 IEEE 12th International Conference on Computer Vision (ICCV)},
  pages = {24--31},
  year = {2009},
  organization = {IEEE}
}
@book{quinlan2014c4,
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  author = {Quinlan, J Ross},
  year = {2014},
  publisher = {Elsevier}
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@inproceedings{sumner2005speeding,
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  author = {Sumner, Marc and Frank, Eibe and Hall, Mark},
  booktitle = {European Conference on Principles of Data Mining and Knowledge Discovery},
  pages = {675--683},
  year = {2005},
  organization = {Springer}
}
@article{wold1984collinearity,
  title = {The collinearity problem in linear regression. The partial least squares (PLS) approach to generalized inverses},
  author = {Wold, Svante and Ruhe, Arnold and Wold, Herman and Dunn, III, WJ},
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  volume = {5},
  number = {3},
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  year = {1984},
  publisher = {SIAM}
}
@article{bair2006prediction,
  title = {Prediction by supervised principal components},
  author = {Bair, Eric and Hastie, Trevor and Paul, Debashis and Tibshirani, Robert},
  journal = {Journal of the American Statistical Association},
  volume = {101},
  number = {473},
  pages = {119--137},
  year = {2006},
  publisher = {Taylor \& Francis}
}
@book{cart84,
  author = {Leo {Breiman} and J. H. {Friedman} and R. A. {Olshen} and C. J. {Stone}},
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  publisher = {Wadsworth Publishing Company},
  address = {Belmont, California, U.S.A.},
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}
@article{hothorn2006unbiased,
  title = {Unbiased recursive partitioning: A conditional inference framework},
  author = {Hothorn, Torsten and Hornik, Kurt and Zeileis, Achim},
  journal = {Journal of Computational and Graphical statistics},
  volume = {15},
  number = {3},
  pages = {651--674},
  year = {2006},
  publisher = {Taylor \& Francis}
}
@article{friedman2000additive,
  title = {Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors)},
  author = {Friedman, Jerome and Hastie, Trevor and Tibshirani, Robert and others},
  journal = {The annals of statistics},
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  number = {2},
  pages = {337--407},
  year = {2000},
  publisher = {Institute of Mathematical Statistics}
}
@article{chan2004lotus,
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  author = {Chan, Kin-Yee and Loh, Wei-Yin},
  journal = {Journal of Computational and Graphical Statistics},
  volume = {13},
  number = {4},
  pages = {826--852},
  year = {2004},
  publisher = {Taylor \& Francis}
}
@book{pages2014multiple,
  title = {Multiple factor analysis by example using R},
  author = {Pag{\`e}s, J{\'e}r{\^o}me},
  year = {2014},
  publisher = {Chapman and Hall/CRC}
}
@book{lebart1995statistique,
  title = {Statistique exploratoire multidimensionnelle},
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  volume = {3},
  year = {1995},
  publisher = {Dunod Paris}
}
@article{opitz1999popular,
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  journal = {Journal of artificial intelligence research},
  volume = {11},
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  year = {1999}
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@article{jordan1994hierarchical,
  title = {Hierarchical mixtures of experts and the EM algorithm},
  author = {Jordan, Michael I and Jacobs, Robert A},
  journal = {Neural computation},
  volume = {6},
  number = {2},
  pages = {181--214},
  year = {1994},
  publisher = {MIT Press}
}
@article{zeileis2008model,
  title = {Model-based recursive partitioning},
  author = {Zeileis, Achim and Hothorn, Torsten and Hornik, Kurt},
  journal = {Journal of Computational and Graphical Statistics},
  volume = {17},
  number = {2},
  pages = {492--514},
  year = {2008},
  publisher = {Taylor \& Francis}
}
@article{landwehr2005logistic,
  title = {Logistic model trees},
  author = {Landwehr, Niels and Hall, Mark and Frank, Eibe},
  journal = {Machine learning},
  volume = {59},
  number = {1-2},
  pages = {161--205},
  year = {2005},
  publisher = {Springer}
}

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