L. Breiman, Random forests, Machine Learning, vol.45, issue.1, pp.5-32, 2001.
DOI : 10.1023/A:1010933404324

G. Chetelat, R. L. Joie, N. Villain, A. Perrotin, V. De-la-sayette et al., Amyloid imaging in cognitively normal individuals, at-risk populations and preclinical Alzheimer's disease, NeuroImage: Clinical, vol.2, pp.356-365, 2013.
DOI : 10.1016/j.nicl.2013.02.006

M. Chupin, A. Hammers, R. S. Liu, O. Colliot, J. Burdett et al., Automatic segmentation of the hippocampus and the amygdala driven by hybrid constraints: Method and validation, NeuroImage, vol.46, issue.3, pp.46749-761, 2009.
DOI : 10.1016/j.neuroimage.2009.02.013
URL : https://hal.archives-ouvertes.fr/hal-00805390

R. S. Doody, R. G. Thomas, M. Farlow, T. Iwatsubo, B. Vellas et al., Phase 3 Trials of Solanezumab for Mild-to-Moderate Alzheimer's Disease, New England Journal of Medicine, vol.370, issue.4, pp.311-321, 2014.
DOI : 10.1056/NEJMoa1312889

B. Dubois, H. Hampel, H. H. Feldman, P. Scheltens, P. Aisen et al., Preclinical Alzheimer's disease: Definition, natural history, and diagnostic criteria Alzheimer's & Dementia, pp.292-323, 2016.

J. Friedman, machine., The Annals of Statistics, vol.29, issue.5, pp.1189-1232, 2001.
DOI : 10.1214/aos/1013203451

J. Friedman, T. Hastie, and R. Tibshirani, Additive logistic regression: a statistical view of boosting. The annals of statistics, pp.337-407, 2000.

J. Friedman, T. Hastie, and R. Tibshirani, Regularization Paths for Generalized Linear Models via Coordinate Descent, Journal of Statistical Software, vol.33, issue.1, 2010.
DOI : 10.18637/jss.v033.i01
URL : http://doi.org/10.18637/jss.v033.i01

J. A. Hardy and G. A. Higgins, Alzheimer's disease: the amyloid cascade hypothesis, Science, vol.256, issue.5054, pp.184-185, 1992.
DOI : 10.1126/science.1566067

A. Hyvarinen, Fast and robust fixed-point algorithms for independent component analysis, IEEE Transactions on Neural Networks, vol.10, issue.3, pp.626-634, 1999.
DOI : 10.1109/72.761722
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.297.8229

P. S. Insel, S. Palmqvist, R. S. Mackin, R. L. Nosheny, O. Hansson et al., Assessing risk for preclinical ?-amyloid pathology with APOE, cognitive, and demographic information Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring, pp.76-84, 2016.
DOI : 10.1016/j.jalz.2016.06.1089

C. Jack, D. Knopman, W. Jagust, L. Shaw, P. Aisen et al., Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade, The Lancet Neurology, vol.9, issue.1, p.119, 2010.
DOI : 10.1016/S1474-4422(09)70299-6

M. M. Mielke, H. J. Wiste, S. D. Weigand, D. S. Knopman, V. J. Lowe et al., Indicators of amyloid burden in a populationbased study of cognitively normal elderly, Neurology, issue.15, pp.791570-1577, 2012.

K. R. Muller, S. Mika, G. Ratsch, K. Tsuda, and B. Scholkopf, An introduction to kernel-based learning algorithms, IEEE Transactions on Neural Networks, vol.12, issue.2, pp.181-201, 2001.
DOI : 10.1109/72.914517

J. T. O-'brien and K. Herholz, Amyloid imaging for dementia in clinical practice, BMC Medicine, vol.13, 2015.