Pages that link to "Item:Q1959647"
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The following pages link to An efficient algorithm for learning to rank from preference graphs (Q1959647):
Displaying 21 items.
- A recursive partitioning method for the prediction of preference rankings based upon Kemeny distances (Q316726) (← links)
- Efficient cross-validation for kernelized least-squares regression with sparse basis expansions (Q439007) (← links)
- Generic subset ranking using binary classifiers (Q714869) (← links)
- Debiased magnitude-preserving ranking: learning rate and bias characterization (Q777111) (← links)
- Reliable graph-based collaborative ranking (Q781894) (← links)
- An experimental comparison of cross-validation techniques for estimating the area under the ROC curve (Q901569) (← links)
- VE dimension induced by Bayesian networks over the Boolean domain (Q903122) (← links)
- Learning intransitive reciprocal relations with kernel methods (Q976508) (← links)
- Learning to rank: a ROC-based graph-theoretic approach (Q976981) (← links)
- On kernel methods for covariates that are rankings (Q1657968) (← links)
- Symmetry and antisymmetry properties of optimal solutions to regression problems (Q1676490) (← links)
- Preference-based learning to rank (Q1959596) (← links)
- Learning to rank on graphs (Q1959630) (← links)
- A review on instance ranking problems in statistical learning (Q2127240) (← links)
- Generalized vec trick for fast learning of pairwise kernel models (Q2127249) (← links)
- Efficient regularized least-squares algorithms for conditional ranking on relational data (Q2251443) (← links)
- Preference Learning and Ranking by Pairwise Comparison (Q3061113) (← links)
- Distributed spectral pairwise ranking algorithms (Q5060714) (← links)
- Learning rates for regularized least squares ranking algorithm (Q5356934) (← links)
- Large-Scale Linear RankSVM (Q5378345) (← links)
- A MIP-based approach to learn MR-sort models with single-peaked preferences (Q6115910) (← links)
