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Hinge loss
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21	CLASSIFICATION WITH A REJECT OPTION USING A HINGE LOSS PETER L. BARTLETT AND MARTEN H. WEGKAMP Abstract. We consider the problem of binary classification where the classifier can, for a particular cost, choose not to cla Add to Reading List Source URL: www.stat.berkeley.edu Language: English - Date: 2006-11-05 19:15:49
22	Perturbation based Large Margin Approach for Ranking Eunho Yang University of Texas at Austin Ambuj Tewari Add to Reading List Source URL: dept.stat.lsa.umich.edu Language: English - Date: 2012-09-12 18:50:24 Machine learning Learning Artificial intelligence Support vector machines Learning to rank Hinge loss Ranking SVM Structured support vector machine Empirical risk minimization Mathematical optimization Supervised learning Distribution
23	Journal of Machine Learning Research1840 Submitted 11/06; Revised 8/07; Published 8/08 Classification with a Reject Option using a Hinge Loss Peter L. Bartlett Add to Reading List Source URL: www.jmlr.org Language: English - Date: 2008-09-02 12:18:56
24	Tabula Rasa: Model Transfer for Object Category Detection Yusuf Aytar Andrew Zisserman Department of Engineering Science University of Oxford {yusuf,az}@robots.ox.ac.uk Add to Reading List Source URL: www.robots.ox.ac.uk Language: English - Date: 2011-08-18 18:19:28 Statistical classification Computer vision Support vector machine Learning Histogram of oriented gradients Silicon Valley Microelectronics One-shot learning Stochastic gradient descent Hinge loss Machine learning Statistics Artificial intelligence
25	Learning Submodular Losses with the Lov´ asz Hinge Jiaqian Yu, Matthew Blaschko To cite this version: Jiaqian Yu, Matthew Blaschko. Learning Submodular Losses with the Lov´asz Hinge. International Conference on Machine Add to Reading List Source URL: hal.inria.fr Language: English Machine learning Submodular set function Order theory Convex analysis Supermodular function Hinge loss Empirical risk minimization Linear programming Vector space Mathematics Mathematical optimization Algebra
26	Max-margin Methods Notation Notation Example images (v1T, v2T,…,v5T)T, where Add to Reading List Source URL: users.cecs.anu.edu.au Language: English - Date: 2009-06-21 14:47:09 Machine learning Support vector machines Information theory Differential geometry Least squares support vector machine Statistics Statistical classification Hinge loss
27	Robust Support Vector Machine Training via Convex Outlier Ablation Linli Xu∗ Koby Crammer Dale Schuurmans Add to Reading List Source URL: webdocs.cs.ualberta.ca Language: English - Date: 2006-05-10 00:04:49 Robust statistics Symbol Hinge loss Outlier Stability Statistics Statistical classification Support vector machine
28	MODIFIED MPE/MMI IN A TRANSDUCER-BASED FRAMEWORK G. Heigold, R. Schl¨uter, and H. Ney Chair of Computer Science 6 - Computer Science Department RWTH Aachen University, Aachen {heigold,schlueter,ney}@cs.rwth-aachen.de AB Add to Reading List Source URL: wiki.inf.ed.ac.uk Language: English - Date: 2009-06-30 07:12:36 Computer accessibility Speech recognition Support vector machine Finite state transducer Margin classifier Hinge loss Mathematical optimization Loss function Statistics Statistical classification Automatic identification and data capture
29	SVM Soft Margin Classifiers: Linear Programming versus Quadratic Programming Qiang Wu [removed] Ding-Xuan Zhou Add to Reading List Source URL: www6.cityu.edu.hk Language: English - Date: 2012-09-11 23:37:55 Hinge loss Quadratic programming Regression analysis Least squares support vector machine Stability Statistics Statistical classification Support vector machines
30	Accelerated Training of Max-Margin Markov Networks with Kernels Xinhua Zhang University of Alberta Alberta Innovates Centre for Machine Learning (AICML) Add to Reading List Source URL: users.cecs.anu.edu.au Language: English - Date: 2011-10-07 08:24:22 Statistics Applied mathematics Artificial intelligence Markov random field Networks Probability and statistics Conditional random field Hinge loss Machine learning Graphical models Theoretical computer science