Abstract
In this chapter, we introduce the statistical learning theory for ranking. In order to better understand existing learning-to-rank algorithms, and to design better algorithms, it is very helpful to deeply understand their theoretical properties. In this chapter, we give the big picture of theoretical analysis for ranking, and point out several important issues to be investigated: statistical ranking framework, generalization ability, and statistical consistency for ranking methods.
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© 2011 Springer-Verlag Berlin Heidelberg
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Liu, TY. (2011). Statistical Learning Theory for Ranking. In: Learning to Rank for Information Retrieval. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14267-3_15
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DOI: https://doi.org/10.1007/978-3-642-14267-3_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14266-6
Online ISBN: 978-3-642-14267-3
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