Pages that link to "Item:Q2905346"
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The following pages link to Training robust support vector regression with smooth non-convex loss function (Q2905346):
Displaying 18 items.
- Homotopy continuation approaches for robust SV classification and regression (Q1697922) (← links)
- Least absolute deviation support vector regression (Q1717816) (← links)
- Calibration of \(\epsilon\)-insensitive loss in support vector machines regression (Q1730072) (← links)
- DC programming and DCA: thirty years of developments (Q1749443) (← links)
- Robust support vector regression in the primal (Q1932127) (← links)
- A novel semi-supervised support vector machine with asymmetric squared loss (Q2036148) (← links)
- Low-rank elastic-net regularized multivariate Huber regression model (Q2049832) (← links)
- Robust support vector regression with generic quadratic nonconvex \(\varepsilon\)-insensitive loss (Q2174697) (← links)
- Robust extreme learning machine in the presence of outliers by iterative reweighted algorithm (Q2177876) (← links)
- Robust regularized extreme learning machine for regression with non-convex loss function via DC program (Q2200186) (← links)
- Kernel-based online regression with canal loss (Q2242215) (← links)
- Maximum likelihood optimal and robust support vector regression with \textit{lncosh} loss function (Q2292212) (← links)
- Support vector machine in the primal space based on the ramp loss function (Q2823756) (← links)
- Convergence rate of SVM for kernel-based robust regression (Q4626547) (← links)
- DC Algorithm for Extended Robust Support Vector Machine (Q5380706) (← links)
- A Robust Regression Framework with Laplace Kernel-Induced Loss (Q5380865) (← links)
- Unified SVM algorithm based on LS-DC loss (Q6134358) (← links)
- A modification piecewise convexification method with a classification strategy for box-constrained non-convex optimization programs (Q6494306) (← links)
