Abstract
The effectiveness of ranking algorithms determine the quality of information retrieval and the goal of ranking algorithms are to learn a real-valued ranking function that induces a ranking or ordering over an instance space. We focused on generalization ability of learning to rank algorithms for information retrieval (IR). As a continuous research of generalization bounds of ranking algorithm, the contribution of this paper includes: generalization bounds for such ranking algorithm via five kinds of stabilities were given. Such stabilities have lower demand than uniform stability and fit for more real applications.
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© 2011 Springer-Verlag Berlin Heidelberg
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Jia, Z., Gao, W., He, X. (2011). Generalization Bounds for Ranking Algorithm via Query-Level Stabilities Analysis. In: Zhu, M. (eds) Information and Management Engineering. ICCIC 2011. Communications in Computer and Information Science, vol 236. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24097-3_31
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DOI: https://doi.org/10.1007/978-3-642-24097-3_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24096-6
Online ISBN: 978-3-642-24097-3
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