Pages that link to "Item:Q2880935"

The following pages link to Robustness and regularization of support vector machines (Q2880935):

Displaying 50 items.

• Regularization of case-specific parameters for robustness and efficiency (Q252778) (← links)
• On the robustness of regularized pairwise learning methods based on kernels (Q325147) (← links)
• Fuzzy one-class classification model using contamination neighborhoods (Q360589) (← links)
• Robustness and generalization (Q420915) (← links)
• On consistency and robustness properties of support vector machines for heavy-tailed distributions (Q440106) (← links)
• Robust quadratic regression and its application to energy-growth consumption problem (Q460299) (← links)
• Support vector machines based on convex risk functions and general norms (Q513637) (← links)
• On qualitative robustness of support vector machines (Q538179) (← links)
• Unified formulation of linear discriminant analysis methods and optimal parameter selection (Q614087) (← links)
• Characterization of the equivalence of robustification and regularization in linear and matrix regression (Q723995) (← links)
• Robust generalized eigenvalue classifier with ellipsoidal uncertainty (Q744726) (← links)
• Using financial risk measures for analyzing generalization performance of machine learning models (Q889281) (← links)
• Using robust dispersion estimation in support vector machines (Q898231) (← links)
• Demonstrating the stability of support vector machines for classification (Q1031290) (← links)
• Analysis of classifiers' robustness to adversarial perturbations (Q1640392) (← links)
• Principal minimax support vector machine for sufficient dimension reduction with contaminated data (Q1660132) (← links)
• Learning kernel logistic regression in the presence of class label noise (Q1676947) (← links)
• Robust K-Median and K-means clustering algorithms for incomplete data (Q1793159) (← links)
• Support vector machines are universally consistent (Q1872633) (← links)
• An efficient algorithm for nonconvex-linear minimax optimization problem and its application in solving weighted maximin dispersion problem (Q2026775) (← links)
• A novel semi-supervised support vector machine with asymmetric squared loss (Q2036148) (← links)
• A robust algorithm for explaining unreliable machine learning survival models using the Kolmogorov-Smirnov bounds (Q2057739) (← links)
• The generalized equivalence of regularization and min-max robustification in linear mixed models (Q2062416) (← links)
• Distributionally robust optimization. A review on theory and applications (Q2074636) (← links)
• Robust regression using support vector regressions (Q2131669) (← links)
• Robust linear classification from limited training data (Q2163210) (← links)
• Frameworks and results in distributionally robust optimization (Q2165596) (← links)
• Sparse high-dimensional regression: exact scalable algorithms and phase transitions (Q2176621) (← links)
• Sparse regression: scalable algorithms and empirical performance (Q2225311) (← links)
• Rejoinder: ``Sparse regression: scalable algorithms and empirical performance'' (Q2225319) (← links)
• On the noise estimation statistics (Q2238618) (← links)
• Recent advances in robust optimization: an overview (Q2256312) (← links)
• Data scarcity, robustness and extreme multi-label classification (Q2320560) (← links)
• Optimality of SVM: novel proofs and tighter bounds (Q2333789) (← links)
• Binary classification SVM-based algorithms with interval-valued training data using triangular and Epanechnikov kernels (Q2418195) (← links)
• Complexity study for the robust stable marriage problem (Q2419115) (← links)
• Robust classification and regression using support vector machines (Q2497274) (← links)
• Robust and distributionally robust optimization models for linear support vector machine (Q2676336) (← links)
• Distributionally robust chance constrained SVM model with \(\ell_2\)-Wasserstein distance (Q2691209) (← links)
• Adversarial classification via distributional robustness with Wasserstein ambiguity (Q2693647) (← links)
• Support vector machines on the D-wave quantum annealer (Q2698831) (← links)
• Robust learning and generalization with support vector machines. (Q2750775) (← links)
• OR forum: An algorithmic approach to linear regression (Q2806052) (← links)
• Statistical optimization in high dimensions (Q2830768) (← links)
• Training robust support vector regression with smooth non-convex loss function (Q2905346) (← links)
• (Q2933956) (← links)
• (Q3093229) (← links)
• Robust Quadratic Programming with Mixed-Integer Uncertainty (Q3386756) (← links)
• Oracle-Based Robust Optimization via Online Learning (Q3450465) (← links)
• (Q4558150) (← links)