BoostingTree: parallel selection of weak learners in boosting, with application to ranking.
- Published in:
- Machine Learning, 2013, v. 93, n. 2-3, p. 293, doi. 10.1007/s10994-013-5364-5
- By:
- Publication type:
- Article
Works matching IS 08856125 AND DT 2013 AND VI 93 AND IP 2-3
Results: 8
1
2
Editorial: Preference learning and ranking.
- Published in:
- 2013
- By:
- Publication type:
- Editorial
3
Tune and mix: learning to rank using ensembles of calibrated multi-class classifiers.
- Published in:
- Machine Learning, 2013, v. 93, n. 2-3, p. 261, doi. 10.1007/s10994-013-5360-9
- By:
- Publication type:
- Article
4
Robust ordinal regression in preference learning and ranking.
- Published in:
- Machine Learning, 2013, v. 93, n. 2-3, p. 381, doi. 10.1007/s10994-013-5365-4
- By:
- Publication type:
- Article
5
Sequential event prediction.
- Published in:
- Machine Learning, 2013, v. 93, n. 2-3, p. 357, doi. 10.1007/s10994-013-5356-5
- By:
- Publication type:
- Article
6
Efficient regularized least-squares algorithms for conditional ranking on relational data.
- Published in:
- Machine Learning, 2013, v. 93, n. 2-3, p. 321, doi. 10.1007/s10994-013-5354-7
- By:
- Publication type:
- Article
7
Calibration and regret bounds for order-preserving surrogate losses in learning to rank.
- Published in:
- Machine Learning, 2013, v. 93, n. 2-3, p. 227, doi. 10.1007/s10994-013-5382-3
- By:
- Publication type:
- Article
8
Supervised clustering of label ranking data using label preference information.
- Published in:
- Machine Learning, 2013, v. 93, n. 2-3, p. 191, doi. 10.1007/s10994-013-5374-3
- By:
- Publication type:
- Article