M. Blaschko, A. Vedaldi, and A. Zisserman, Simultaneous object detection and ranking with weak supervision, NIPS, 2010.

N. Dalal and B. Triggs, Histograms of oriented gradients for human detection, CVPR, 2005.
URL : https://hal.archives-ouvertes.fr/inria-00548512

A. Dempster, N. Laird, R. , and D. , Maximum likelihood from incomplete data via the EM algorithm, Journal of Royal Statistical Society, 1977.

P. Felzenszwalb, D. Mcallester, and D. And-ramanan, A discriminatively trained, multiscale, deformable part model, CVPR, 2008.

T. Joachims, T. Finley, Y. , and C. , Cutting-plane training for structural SVMs, Machine Learning, 2009.

M. P. Kumar, B. Packer, and D. Koller, Self-paced learning for latent variable models, NIPS, 2010.

M. P. Kumar, H. Turki, D. Preston, and D. Koller, Learning specific-class segmentation from diverse data, ICCV, 2011.

S. Maji, L. Bourdev, M. , and J. , Action recognition from a distributed representation of pose and appearance, CVPR, 2011.

K. Miller, M. P. Kumar, B. Packer, D. Goodman, and D. Koller, Max-margin min-entropy models, AIS- TATS, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00773602

C. Rao, Diversity and dissimilarity coefficients: A unified approach, Theoretical Population Biology, 1982.

S. Shalev-shwartz, Y. Singer, and N. Srebro, Pegasos: Primal estimated sub-gradient solver for SVM, ICML, 2009.

R. Sundberg, Maximum likelihood theory for incomplete data from an exponential family, Scandinavian Journal of Statistics, 1974.

I. Tsochantaridis, T. Hofmann, Y. Altun, J. , and T. , Support vector machine learning for interdependent and structured output spaces, ICML, 2004.

C. Yu and T. Joachims, Learning structural SVMs with latent variables, ICML, 2009.
DOI : 10.1145/1553374.1553523
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.147.1203