Robust Truncated Hinge Loss Support Vector Machines
From MaRDI portal
Publication:3632564
DOI10.1198/016214507000000617zbMath1469.62293OpenAlexW2089394015MaRDI QIDQ3632564
Publication date: 12 June 2009
Published in: Journal of the American Statistical Association (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1198/016214507000000617
Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05)
Related Items
Proximal operator and optimality conditions for ramp loss SVM, D-learning to estimate optimal individual treatment rules, CTSVM: a robust twin support vector machine with correntropy-induced loss function for binary classification problems, Probability-enhanced effective dimension reduction for classifying sparse functional data, Statistical analysis of distance estimators with density differences and density ratios, Network-based naive Bayes model for social network, Training robust support vector regression with smooth non-convex loss function, Robust relevance vector machine for classification with variational inference, Global Convergence of Stochastic Gradient Hamiltonian Monte Carlo for Nonconvex Stochastic Optimization: Nonasymptotic Performance Bounds and Momentum-Based Acceleration, Modeling, forecasting and trading the EUR exchange rates with hybrid rolling genetic algorithms -- support vector regression forecast combinations, Identifying optimal biomarker combinations for treatment selection via a robust kernel method, Supervised classification and mathematical optimization, Learning rates of kernel-based robust classification, A kernel learning framework for domain adaptation learning, Auxiliary Marker-Assisted Classification in the Absence of Class Identifiers, Feature selection for linear SVMs under uncertain data: robust optimization based on difference of convex functions algorithms, Robust support vector regression in the primal, Robust and distributionally robust optimization models for linear support vector machine, Estimating individualized treatment rules with risk constraint, A robust outlier control framework for classification designed with family of homotopy loss function, Solution methodologies for minimizing a sum of pointwise minima of two functions, Robust support vector quantile regression with truncated pinball loss (RSVQR), Robust multicategory support matrix machines, Unified SVM algorithm based on LS-DC loss, Robust logistic regression with shift parameter estimation, Reverse adaptive krill herd locally weighted support vector regression for forecasting and trading exchange traded funds, Variable selection for multicategory SVM via adaptive sup-norm regularization, Homotopy continuation approaches for robust SV classification and regression, Robust estimation in regression and classification methods for large dimensional data, Data-adaptive discriminative feature localization with statistically guaranteed interpretation, Transfer Learning of Individualized Treatment Rules from Experimental to Real-World Data, Set-Valued Support Vector Machine with Bounded Error Rates, Adversarial classification via distributional robustness with Wasserstein ambiguity, On minimum distribution discrepancy support vector machine for domain adaptation, Unnamed Item, Robust Support Vector Machines for Classification with Nonconvex and Smooth Losses, DC Algorithm for Extended Robust Support Vector Machine, Heuristic approaches for support vector machines with the ramp loss, Query-dependent ranking and its asymptotic properties, Extended Robust Support Vector Machine Based on Financial Risk Minimization, Convergence rate of SVM for kernel-based robust regression, Unnamed Item, Instance weighting through data imprecisiation, Assessing robustness of classification using an angular breakdown point, DC programming and DCA: thirty years of developments, Robust multicategory support vector machines using difference convex algorithm, Adaptively weighted large-margin angle-based classifiers, The landscape of empirical risk for nonconvex losses, A bilateral-truncated-loss based robust support vector machine for classification problems, Optimization approaches to supervised classification, Non-crossing large-margin probability estimation and its application to robust SVM via pre\-condi\-tion\-ing, Quadratic Majorization for Nonconvex Loss with Applications to the Boosting Algorithm, Robust boosting with truncated loss functions, MM for penalized estimation, Multicategory large margin classification methods: hinge losses vs. coherence functions, Comments on: ``Probability enhanced effective dimension reduction for classifying sparse functional data, Fuzzy quadratic surface support vector machine based on Fisher discriminant analysis, Robust support vector machines based on the rescaled hinge loss function, Maximum likelihood optimal and robust support vector regression with \textit{lncosh} loss function, Robustness of learning algorithms using hinge loss with outlier indicators, Adaptive Weighted Learning for Unbalanced Multicategory Classification, Variable smoothing incremental aggregated gradient method for nonsmooth nonconvex regularized optimization, Relaxing support vectors for classification, Nonlinear vertex discriminant analysis with reproducing kernels, Robust penalized logistic regression with truncated loss functions, Nonnegative estimation and variable selection under minimax concave penalty for sparse high-dimensional linear regression models, Estimation of Individualized Decision Rules Based on an Optimized Covariate-Dependent Equivalent of Random Outcomes, Sparse additive machine with ramp loss, Learning Optimal Distributionally Robust Individualized Treatment Rules, Nonregular and minimax estimation of individualized thresholds in high dimension with binary responses, Large scale analysis of generalization error in learning using margin based classification methods, Learning sparse classifiers with difference of convex functions algorithms, Linear convergence of proximal incremental aggregated gradient method for nonconvex nonsmooth minimization problems, Quadratic Convergence of Smoothing Newton's Method for 0/1 Loss Optimization, Regularization of case-specific parameters for robustness and efficiency, Probability estimation with machine learning methods for dichotomous and multicategory outcome: Theory
