A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Support vector machines are universally consistent
Journal of Complexity
Learning using hidden information (learning with teacher)
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Hybrid artificial neural networks: models, algorithms and data
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
Enhanced default risk models with SVM+
Expert Systems with Applications: An International Journal
Privileged information for data clustering
Information Sciences: an International Journal
Learning using privileged information in prototype based models
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
Learning Using Privileged Information with L-1 Support Vector Machine
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 03
Boosting with side information
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
Early experiments with neural diversity machines
Neurocomputing
Incremental hierarchical text clustering with privileged information
Proceedings of the 2013 ACM symposium on Document engineering
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In the Afterword to the second edition of the book ''Estimation of Dependences Based on Empirical Data'' by V. Vapnik, an advanced learning paradigm called Learning Using Hidden Information (LUHI) was introduced. This Afterword also suggested an extension of the SVM method (the so called SVM"@c+ method) to implement algorithms which address the LUHI paradigm (Vapnik, 1982-2006, Sections 2.4.2 and 2.5.3 of the Afterword). See also (Vapnik, Vashist, & Pavlovitch, 2008, 2009) for further development of the algorithms. In contrast to the existing machine learning paradigm where a teacher does not play an important role, the advanced learning paradigm considers some elements of human teaching. In the new paradigm along with examples, a teacher can provide students with hidden information that exists in explanations, comments, comparisons, and so on. This paper discusses details of the new paradigm and corresponding algorithms, introduces some new algorithms, considers several specific forms of privileged information, demonstrates superiority of the new learning paradigm over the classical learning paradigm when solving practical problems, and discusses general questions related to the new ideas.