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Author Clémençon, Stéphan ♦ Robbiano, Sylvain
Source CiteSeerX
Content type Text
File Format PDF
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Main Purpose ♦ Complexity Condition ♦ Bound Result ♦ Plug-in Rule ♦ Minimax Perspective ♦ Nested Continuous Collection ♦ Standard Binary Classification Setup ♦ Bipartite Ranking ♦ Ranking Rule ♦ Cost-sensitive Classification Problem ♦ Present Paper ♦ Specific Situation ♦ Suitable Margin Assumption ♦ Global Low Noise Condition ♦ Super-fast Rate ♦ Wide Nonparametric Class ♦ Regression Function ♦ Plug-in Classifier
Description While it is now well-known in the standard binary classification setup, that, under suitable margin assumptions and complexity conditions on the regression function, fast or even super-fast rates (i.e. rates faster than n −1/2 or even faster than n −1) can be achieved by plug-in classifiers, no result of this nature has been proved yet in the context of bipartite ranking, though akin to that of classification. It is the main purpose of the present paper to investigate this issue, by considering bipartite ranking as a nested continuous collection of cost-sensitive classification problems. A global low noise condition is exhibited under which certain (plugin) ranking rules are proved to achieve fast (but not super-fast) rates over a wide nonparametric class of models. A lower bound result is also stated in a specific situation, establishing that such rates are optimal from a minimax perspective. 1.
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article
Publisher Date 2011-01-01
Publisher Institution In Proceedings of ICML 11