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The learning with privileged information setting has recently attracted a lot of attention within the machine learning community, as it allows the integration of additional knowledge into the training process of a classifier, even when this comes in the form of a data modality that is not available at test time.
The lack of a priori distinctions between learning algorithms
D.H. Wolpert · 1996
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Regression with input-dependent noise: A gaussian process treatment
P. W. Goldberg, C. K. I. Williams, and C. M. Bishop · 1998
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Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
B. Scholkopf and A. J. Smola · 2001
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A Family of Algorithms for Approximate Bayesian Inference
T. P. Minka · 2001
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Assessing approximate inference for binary Gaussian process classification
M. Kuss and C. E. Rasmussen · 2005
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Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
C. E. Rasmussen and C. K. I. Williams · 2006
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Expectation propagation for exponential families
M. Seeger · 2006
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Statistical comparisons of classifiers over multiple data sets
J. Demšar · 2006
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Approximations for Binary Gaussian Process Classification
H. Nickisch and C. E. Rasmussen · 2008
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Speeded-up robust features (surf)
H. Bay, A. Ess, T. Tuytelaars, and L. Van Gool · 2008
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A new learning paradigm: Learning using privileged information
V. Vapnik and A. Vashist · 2009
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Kernel conditional quantile estimation via reduction revisited
N. Quadrianto, K. Kersting, M. D. Reid, T. S. Caetano, and W. L. Buntine · 2009
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On the theory of learning with privileged information
D. Pechyony and V. Vapnik · 2010
Cited alongside, same era.
Financial distress model prediction using SVM+
B. Ribeiro, C. Silva, A. Vieira, A. Gaspar-Cunha, and J.C. das Neves · 2010
Cited alongside, same era.
Automatic attribute discovery and characterization from noisy web data
T. L. Berg, A. C. Berg, and J. Shih · 2010
Cited alongside, same era.
Learning to rank using privileged information
V. Sharmanska, N. Quadrianto, and C. H. Lampert · 2013
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Incorporating privileged information through metric learning
S. Fouad, P. Tino, S. Raychaudhury, and P. Schneider · 2013
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Distributed representations of words and phrases and their compositionality
T. Mikolov, I. Sutskever, K. Chen, G. Corrado, and J. Dean · 2013
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Nested Expectation Propagation for Gaussian Process Classification with a Multinomial Probit Likelihood
J. Riihimäki, P. Jylänki, and A. Vehtari · 2013
Later among the works it cites.
Learning using privileged information: SVM+ and weighted SVM
M. Lapin, M. Hein, and B. Schiele · 2014
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Distributed representations of sentences and documents
Q. V. Le and T. Mikolov · 2014
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Fast optimization algorithms for solving SVM+
D. Pechyony and V. Vapnik · 2011
Cited alongside, same era.
Variational heteroscedastic gaussian process regression
M. Lázaro-Gredilla and M. K. Titsias · 2011
Cited alongside, same era.
Privileged information for data clustering
J. Feyereisl and U. Aickelin · 2012
Cited alongside, same era.
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Attribute-based classification for zero-shot visual object categorization
C. H. Lampert, H. Nickisch, and S. Harmeling · 2014
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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
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