Pages that link to "Item:Q2642921"
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The following pages link to How to compare different loss functions and their risks (Q2642921):
Displaying 24 items.
- Quantile regression with \(\ell_1\)-regularization and Gaussian kernels (Q457695) (← links)
- Estimating conditional quantiles with the help of the pinball loss (Q637098) (← links)
- Learning rates for kernel-based expectile regression (Q669274) (← links)
- Soft-max boosting (Q747255) (← links)
- Robust learning from bites for data mining (Q1020821) (← links)
- Risk-sensitive loss functions for sparse multi-category classification problems (Q1031681) (← links)
- An investigation for loss functions widely used in machine learning (Q1644339) (← links)
- Feasible generalized least squares using support vector regression (Q1714071) (← links)
- Calibration of \(\epsilon\)-insensitive loss in support vector machines regression (Q1730072) (← links)
- Loss functions for loss given default model comparison (Q1754331) (← links)
- Calibrated asymmetric surrogate losses (Q1950846) (← links)
- Honest variable selection in linear and logistic regression models via \(\ell _{1}\) and \(\ell _{1}+\ell _{2}\) penalization (Q1951794) (← links)
- Multiclass classification, information, divergence and surrogate risk (Q1990579) (← links)
- Calibration and regret bounds for order-preserving surrogate losses in learning to rank (Q2251435) (← links)
- The asymptotics of ranking algorithms (Q2438754) (← links)
- Employing different loss functions for the classification of images via supervised learning (Q2440581) (← links)
- Consistency and robustness of kernel-based regression in convex risk minimization (Q2469652) (← links)
- Composite binary losses (Q2896150) (← links)
- Analysis of Regression Algorithms with Unbounded Sampling (Q3386411) (← links)
- A Framework of Learning Through Empirical Gain Maximization (Q5004380) (← links)
- (Q5053228) (← links)
- Positive-unlabeled classification under class-prior shift: a prior-invariant approach based on density ratio estimation (Q6106437) (← links)
- An error analysis for deep binary classification with sigmoid loss (Q6588360) (← links)
- Optimality of robust online learning (Q6645952) (← links)
