The following pages link to Learning Theory (Q5473606):
Displaying 17 items.
- Adaptive partitioning schemes for bipartite ranking (Q413849) (← links)
- Learning rate of support vector machine for ranking (Q468458) (← links)
- Overlaying classifiers: A practical approach to optimal scoring (Q607486) (← links)
- Robust reductions from ranking to classification (Q1009271) (← links)
- A confidence voting process for ranking problems based on support vector machines (Q1026549) (← links)
- An efficient algorithm for learning to rank from preference graphs (Q1959647) (← links)
- A review on instance ranking problems in statistical learning (Q2127240) (← links)
- Calibration and regret bounds for order-preserving surrogate losses in learning to rank (Q2251435) (← links)
- Row and column generation algorithms for minimum margin maximization of ranking problems (Q2799660) (← links)
- Row and Column Generation Algorithm for Maximization of Minimum Margin for Ranking Problems (Q2806938) (← links)
- The \(p\)-norm push: a simple convex ranking algorithm that concentrates at the top of the list (Q2880971) (← links)
- Applications of concentration inequalities for statistical scoring and ranking problems (Q3451708) (← links)
- (Q4369087) (← links)
- (Q4558187) (← links)
- Stochastically Constrained Ranking and Selection via SCORE (Q5270725) (← links)
- Empirical Likelihood and Ranking Methods (Q5272956) (← links)
- On the convergence rate and some applications of regularized ranking algorithms (Q5963450) (← links)
