378 search results - page 9 / 76 » Learning to Rank with Supplementary Data |
115
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ICML
2005
IEEE
16 years 4 months ago
2005
IEEE
We investigate using gradient descent methods for learning ranking functions; we propose a simple probabilistic cost function, and we introduce RankNet, an implementation of these...
127
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ICML
2010
IEEE
15 years 4 months ago
2010
IEEE
This paper introduces two new methods for label ranking based on a probabilistic model of ranking data, called the Plackett-Luce model. The idea of the first method is to use the ...
121
Voted
ESANN
2004
15 years 4 months ago
2004
Input feature ranking and selection represent a necessary preprocessing stage in classification, especially when one is required to manage large quantities of data. We introduce a ...
123
Voted
KDD
2005
ACM
16 years 3 months ago
2005
ACM
This paper presents a novel approach for using clickthrough data to learn ranked retrieval functions for web search results. We observe that users searching the web often perform ...
115
Voted
CIKM
2000
Springer
15 years 7 months ago
2000
Springer
RankBoost is a recently proposed algorithm for learning ranking functions. It is simple to implement and has strong justifications from computational learning theory. We describe...