L. Bottou and O. Bousquet, The tradeoffs of large scale learning, NIPS, 2007.

L. Breiman, Bagging predictors, Machine Learning, vol.10, issue.2, 1996.
DOI : 10.1007/BF00058655

J. Cervantes, X. Li, W. Yu, and J. Bejarano, Multi-class support vector machines for large data sets via minimum enclosing ball clustering. Electrical and Electronics Engineering ICEEE, 4th International Conference on, 2007.

S. Moses and . Charikar, Similarity estimation techniques from rounding algorithms, STOC '02: Proceedings of the thiry-fourth annual ACM symposium on Theory of computing, 2002.

J. Deng, W. Dong, R. Socher, L. Li, K. Li et al., ImageNet: A Large-Scale Hierarchical Image Database, CVPR, 2009.

J. Deng, A. C. Berg, K. Li, and L. Fei-fei, What Does Classifying More Than 10,000 Image Categories Tell Us?, Proceedings of the 11th European conference on Computer vision: Part V, 2010.
DOI : 10.1007/978-3-642-15555-0_6

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.173.3680

K. Rong-en-fan, C. Chang, X. Hsieh, C. Wang, and . Lin, LIBLINEAR: A library for large linear classification, Journal of Machine Learning Research, 2008.

T. Gao and D. Koller, Discriminative learning of relaxed hierarchy for largescale visual recognition, ICCV, 2011.

S. Godbole, S. Sarawagi, and S. Chakrabarti, Scaling multi-class support vector machines using inter-class confusion, Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '02, 2002.
DOI : 10.1145/775047.775122

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.18.5578

P. Jain, S. Vijayanarasimhan, and K. Grauman, Hashing hyperplane queries to near points with applications to large-scale active learning, NIPS, 2010.

T. Joachims, Training linear SVMs in linear time, Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '06, 2006.
DOI : 10.1145/1150402.1150429

H. Kim, S. Pang, H. Je, D. Kim, and S. Y. Bang, Support Vector Machine Ensemble with Bagging, Proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines, 2002.
DOI : 10.1007/3-540-45665-1_31

B. Kulis and K. Grauman, Kernelized locality-sensitive hashing for scalable image search, 2009 IEEE 12th International Conference on Computer Vision, 2009.
DOI : 10.1109/ICCV.2009.5459466

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.167.4414

P. Li, A. Shrivastava, J. L. Moore, and A. C. König, Hashing algorithms for large-scale learning

Q. Guo-jun-qi, T. Tian, and . Huang, Locality-sensitive support vector machine by exploring local correlation and global regularization, Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition, 2011.

A. F. Smeaton, P. Over, and W. Kraaij, High-Level Feature Detection from Video in TRECVid: A 5-Year Retrospective of Achievements, Multimedia Content Analysis, Theory and Applications, 2008.
DOI : 10.1007/978-0-387-76569-3_6

R. Troncy, B. Malocha, and A. T. Fialho, Linking events with media, Proceedings of the 6th International Conference on Semantic Systems, I-SEMANTICS '10, 2010.
DOI : 10.1145/1839707.1839759

. Shi-jin, A. Wang, Y. Mathew, L. Chen, L. Xi et al., Empirical analysis of support vector machine ensemble classifiers, Expert Syst. Appl, 2009.

C. Y. Jian-xiong-dong, A. Suen, and . Krzyzak, Effective shrinkage of large multi-class linear svm models for text categorization, 2008 19th International Conference on Pattern Recognition, 2008.
DOI : 10.1109/ICPR.2008.4761782

H. Yu, C. Hsieh, K. Chang, and C. Lin, Large linear classification when data cannot fit in memory, ACM Trans. Knowl. Discov. Data, 2012.