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The Support Vector Machine using Privileged Information (SVM+) has been proposed to train a classifier to utilize the additional privileged information that is only available in the training phase but not available in the test phase.
Support-vector networks
Corinna Cortes and Vladimir Vapnik · 1995
Earlier work this paper cites.
Making large-scale SVM learning practical
T. Joachims · 1999
Earlier work this paper cites.
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
Nello Cristianini and John Shawe-Taylor · 2000
Earlier work this paper cites.
LIBSVM:a library for support vector machines
Chih-Chung Chang and Chih-Jen Lin · 2001
Earlier work this paper cites.
Two view learning: Svm-2k, theory and practice
Jason D. R. Farquhar, David R. Hardoon, Hongying Meng, John Shawe-Taylor, and Sándor Szedmák · 2005
Earlier work this paper cites.
Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories
Fei-Fei Li, Robert Fergus, and Pietro Perona · 2007
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Fei-Fei Li · 2009
Earlier work this paper cites.
A new learning paradigm: Learning using privileged information
Vladimir Vapnik and Akshay Vashist · 2009
Earlier work this paper cites.
Improving web image search results using query-relative classifiers
Josip Krapac, Moray Allan, Jakob J. Verbeek, and Frédéric Jurie · 2010
Earlier work this paper cites.
On the theory of learnining with privileged information
Dmitry Pechyony and Vladimir Vapnik · 2010
Cited alongside, same era.
Smo-style algorithms for learning using privileged information
Dmitry Pechyony, Rauf Izmailov, Akshay Vashist, and Vladimir Vapnik · 2010
Cited alongside, same era.
Privileged information for data clustering
Jan Feyereisl and Uwe Aickelin · 2012
Cited alongside, same era.
Learning using privileged information in prototype based models
Shereen Fouad, Peter Tiño, Somak Raychaudhury, and Petra Schneider · 2012
Cited alongside, same era.
Incorporating privileged information through metric learning
Shereen Fouad, Peter Tiño, Somak Raychaudhury, and Petra Schneider · 2013
Cited alongside, same era.
Learning to rank using privileged information
Viktoriia Sharmanska, Novi Quadrianto, and Christoph H. Lampert · 2013
Cited alongside, same era.
Decaf: A deep convolutional activation feature for generic visual recognition
J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, and T. Darrell · 2014
Later among the works it cites.
Mind the nuisance: Gaussian process classification using privileged noise
Daniel Hernández-Lobato, Viktoriia Sharmanska, Kristian Kersting, Christoph H. Lampert, and Novi Quadrianto · 2014
Later among the works it cites.
Learning using privileged information: SVM+ and weighted SVM
Maksim Lapin, Matthias Hein, and Bernt Schiele · 2014
Later among the works it cites.
Unifying distillation and privileged information
David Lopez-Paz, Léon Bottou, Bernhard Schölkopf, and Vladimir Vapnik · 2015
Later among the works it cites.
Learning using privileged information: Similarity control and knowledge transfer
Vladimir Vapnik and Rauf Izmailov · 2015
Later among the works it cites.
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Soft margin multiple kernel learning
Xinxing Xu, Ivor W. Tsang, and Dong Xu · 2013
Cited alongside, same era.
Return of the devil in the details: Delving deep into convolutional nets
K. Chatfield, K. Simonyan, A. Vedaldi, and A. Zisserman · 2014
Cited alongside, same era.
Matconvnet: Convolutional neural networks for MATLAB
Andrea Vedaldi and Karel Lenc · 2015
Later among the works it cites.
Xinxing Xu, Wen Li, and Dong Xu · 2015
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Action and event recognition in videos by learning from heterogeneous web sources
Li Niu, Xinxing Xu, Lin Chen, Lixin Duan, and Dong Xu · 2016
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