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NIPS
2003

Learning a Distance Metric from Relative Comparisons

8 years 7 months ago
Learning a Distance Metric from Relative Comparisons
This paper presents a method for learning a distance metric from relative comparison such as “A is closer to B than A is to C”. Taking a Support Vector Machine (SVM) approach, we develop an algorithm that provides a flexible way of describing qualitative training data as a set of constraints. We show that such constraints lead to a convex quadratic programming problem that can be solved by adapting standard methods for SVM training. We empirically evaluate the performance and the modelling flexibility of the algorithm on a collection of text documents.
Matthew Schultz, Thorsten Joachims
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2003
Where NIPS
Authors Matthew Schultz, Thorsten Joachims
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