DMCA
Learning to rank using gradient descent (2005)
Cached
Download Links
- [imls.engr.oregonstate.edu]
- [www.machinelearning.org]
- DBLP
Other Repositories/Bibliography
by
Chris Burges
,
Tal Shaked
,
Erin Renshaw
,
Matt Deeds
,
Nicole Hamilton
,
Greg Hullender
| Venue: | In ICML |
| Citations: | 532 - 17 self |
BibTeX
@INPROCEEDINGS{Burges05learningto,
author = {Chris Burges and Tal Shaked and Erin Renshaw and Matt Deeds and Nicole Hamilton and Greg Hullender},
title = {Learning to rank using gradient descent},
booktitle = {In ICML},
year = {2005},
pages = {89--96}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
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 ideas using a neural network to model the underlying ranking function. We present test results on toy data and on data from a commercial internet search engine. 1.
