378 search results - page 9 / 76 » Learning to Rank with Supplementary Data |
ICML
2005
IEEE
14 years 8 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...
ICML
2010
IEEE
13 years 9 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 ...
ESANN
2004
13 years 9 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 ...
KDD
2005
ACM
14 years 8 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 ...
CIKM
2000
Springer
14 years 6 days 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...