Neural Networks for Pattern Recognition, 1995. ,
Kernel methods for pattern recognition, 2004. ,
A Probabilistic Theory of Pattern Recognition, 1996. ,
DOI : 10.1007/978-1-4612-0711-5
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2009. ,
Machine Learning: A Probabilistic Perspective ,
Boosting: Foundations and Algorithms ,
The Nature of Statistical Learning Theory, 1995. ,
Statistical Learning Theory, 1998. ,
Random search for hyper-parameter optimization, Journal of Machine Learning Research, 2012. ,
Gradient-Based Optimization of Hyperparameters, Neural Computation, vol.58, issue.8, pp.1889-1900, 2000. ,
DOI : 10.1038/317314a0
Unsupervised feature learning and deep learning: A review and new perspectives, 1206. ,
Natural language processing (almost) from scratch, Journal of Machine Learning Research, vol.12, pp.2493-2537, 2011. ,
ImageNet classification with deep convolutional neural networks, Advances in Neural Information Processing Systems. 2012 ,
Algorithms for hyperparameter optimization, Advances in Neural Information Processing Systems (NIPS), 2011. ,
URL : https://hal.archives-ouvertes.fr/hal-00642998
Practical Bayesian optimization of machine learning algorithms, Advances in Neural Information Processing Systems, 2012. ,
Auto-WEKA, Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '13, 2012. ,
DOI : 10.1145/2487575.2487629
Collaborative hyperparameter tuning, International Conference on Machine Learning (ICML), 2013. ,
URL : https://hal.archives-ouvertes.fr/in2p3-00907381
The densest hemisphere problem, Theoretical Computer Science, vol.6, issue.1, pp.93-107, 1978. ,
DOI : 10.1016/0304-3975(78)90006-3
Learning representations by back-propagating errors, Nature, vol.85, issue.6088, pp.533-536, 1986. ,
DOI : 10.1038/323533a0
A training algorithm for optimal margin classifiers, Proceedings of the fifth annual workshop on Computational learning theory , COLT '92, pp.144-152, 1992. ,
DOI : 10.1145/130385.130401
Support-vector networks, Machine Learning, pp.273-297, 1995. ,
DOI : 10.1007/BF00994018
A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting, Journal of Computer and System Sciences, vol.55, issue.1, pp.119-139, 1997. ,
DOI : 10.1006/jcss.1997.1504
The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network, IEEE Transactions on Information Theory, vol.44, issue.2, pp.525-536, 1998. ,
DOI : 10.1109/18.661502
Netlab: Algorithms for Pattern Recognition, 2002. ,
The tradeoffs of large scale learning, Advances in Neural Information Processing Systems, pp.161-168, 2008. ,
Boosting algorithms as gradient descent, Advances in Neural Information Processing Systems, pp.512-518, 2000. ,
Improved generalization through explicit optimization of margins, Machine Learning, pp.243-255, 2000. ,
Logistic regression, AdaBoost and Bregman distances, Machine Learning, pp.253-285, 2002. ,
Boosting the margin: a new explanation for the effectiveness of voting methods, The Annals of Statistics, vol.26, issue.5, pp.1651-1686, 1998. ,
DOI : 10.1214/aos/1024691352
Improved boosting algorithms using confidence-rated predictions, Proceedings of the eleventh annual conference on Computational learning theory , COLT' 98, pp.297-336, 1999. ,
DOI : 10.1145/279943.279960
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.156.2440
Robust Real-Time Face Detection, International Journal of Computer Vision, vol.57, issue.2, pp.137-154, 2004. ,
DOI : 10.1023/B:VISI.0000013087.49260.fb
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.102.9805
Yahoo! Learning-to-Rank Challenge overview, Yahoo! Learning-to-Rank Challenge (JMLR W&CP), pp.1-24, 2011. ,
reCAPTCHA: Human-Based Character Recognition via Web Security Measures, Science, vol.321, issue.5895, pp.1465-1468, 2008. ,
DOI : 10.1126/science.1160379
Labeling images with a computer game, Conference on Human factors in computing systems (CHI04), pp.319-326, 2004. ,
Games with a Purpose, Computer, vol.39, issue.6, 2006. ,
DOI : 10.1109/MC.2006.196
Nonlinear Dimensionality Reduction by Locally Linear Embedding, Science, vol.290, issue.5500, pp.2323-2326, 2000. ,
DOI : 10.1126/science.290.5500.2323
A Global Geometric Framework for Nonlinear Dimensionality Reduction, Science, vol.290, issue.5500, pp.2319-2323, 2000. ,
DOI : 10.1126/science.290.5500.2319
The Netflix prize, 2007. ,
Geometry in sound: A speech/music audio classifier inspired by an image classifier, International Computer Music Conference, 2005. ,
Aggregate features and ADABOOST for music classification, Machine Learning, vol.10, issue.5, pp.473-484, 2006. ,
DOI : 10.1007/s10994-006-9019-7
URL : https://hal.archives-ouvertes.fr/inria-00176062
Pierre Auger project design report, Tech. Rep, 1997. ,
Ranking by calibrated AdaBoost, Yahoo! Ranking Challenge, pp.37-48, 2010. ,
URL : https://hal.archives-ouvertes.fr/hal-00643001
A Robust Ranking Methodology Based on Diverse Calibration of AdaBoost, European Conference on Machine Learning, pp.263-279, 2011. ,
DOI : 10.1007/978-3-642-23780-5_27
URL : https://hal.archives-ouvertes.fr/hal-00643000
Efficient, reliable and fast high-level triggering using a bonsai boosted decision tree, Journal of Instrumentation, vol.8, issue.02, 2012. ,
DOI : 10.1088/1748-0221/8/02/P02013
A single track HLT1 trigger, 2011. ,
Robust Object Detection via Soft Cascade, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), pp.236-243, 2005. ,
DOI : 10.1109/CVPR.2005.310
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.190.3554
Boosting chain learning for object detection, Ninth IEEE International Conference on Computer Vision, pp.709-715, 2003. ,
WaldBoost ??? Learning for Time Constrained Sequential Detection, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), pp.150-156, 2005. ,
DOI : 10.1109/CVPR.2005.373
Learning when to stop thinking and do something!, Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09, pp.825-832, 2009. ,
DOI : 10.1145/1553374.1553480
Boosting classifier cascades, Advances in Neural Information Processing Systems 23, pp.2047-2055, 2010. ,
Fast classification using sparse decision DAGs, International Conference on Machine Learning, 2012. ,
URL : https://hal.archives-ouvertes.fr/in2p3-00711150
Observation of Single Top-Quark Production, Physical Review Letters, vol.103, issue.9, 2009. ,
DOI : 10.1103/PhysRevLett.103.092001
URL : https://hal.archives-ouvertes.fr/in2p3-00365919
Observation of Electroweak Single Top-Quark Production, Physical Review Letters, vol.103, issue.9, p.92002, 2009. ,
DOI : 10.1103/PhysRevLett.103.092002
URL : https://hal.archives-ouvertes.fr/in2p3-00366602
Asymptotic formulae for likelihood-based tests of new physics, The European Physical Journal C, vol.10, issue.3, pp.1-19, 2011. ,
DOI : 10.1140/epjc/s10052-011-1554-0