Training a Support Vector Machine in the Primal

From MaRDI portal
Publication:5294324

DOI10.1162/neco.2007.19.5.1155zbMath1123.68101OpenAlexW2147898188WikidataQ47295698 ScholiaQ47295698MaRDI QIDQ5294324

Olivier Chapelle

Publication date: 24 July 2007

Published in: Neural Computation (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/11858/00-001M-0000-0013-CC0B-D




Related Items (56)

Gaining Outlier Resistance With Progressive Quantiles: Fast Algorithms and Theoretical StudiesRecurrent Kernel Machines: Computing with Infinite Echo State NetworksBinary separation and training support vector machinesAsymmetric \(\nu\)-tube support vector regressionTraining robust support vector regression with smooth non-convex loss functionNew smoothing SVM algorithm with tight error bound and efficient reduced techniquesUnnamed ItemNondegenerate Piecewise Linear Systems: A Finite Newton Algorithm and Applications in Machine LearningSupervised classification and mathematical optimizationInhibition in Multiclass ClassificationSmoothly approximated support vector domain descriptionCross-term elimination in Wigner distribution based on 2D signal processing techniquesRobust support vector regression in the primalOrthogonal optimal reverse prediction for semi-supervised learningParsimonious kernel extreme learning machine in primal via Cholesky factorizationLearning a distance metric from relative comparisons between quadruplets of imagesBlessing of dimensionality at the edge and geometry of few-shot learningRobust formulations for clustering-based large-scale classificationValue-at-risk support vector machine: stability to outliersFast PRISM: branch and bound Hough transform for object class detectionSemi-supervised active learning for support vector machines: a novel approach that exploits structure information in dataComputational complexity of kernel-based density-ratio estimation: a condition number analysisCorrection of AI systems by linear discriminants: probabilistic foundationsPegasos: primal estimated sub-gradient solver for SVMBridging logic and kernel machinesA superlinearly convergent \(R\)-regularized Newton scheme for variational models with concave sparsity-promoting priorsFast construction of correcting ensembles for legacy artificial intelligence systems: algorithms and a case studyGraph regularization methods for Web spam detectionLeast absolute deviation support vector regressionRobust Support Vector Machines for Classification with Nonconvex and Smooth LossesTraining Lp norm multiple kernel learning in the primalAn efficient augmented Lagrangian method for support vector machineStatistics for data with geometric structure. Abstracts from the workshop held January 21--27, 2018How Effectively Train Large-Scale Machine Learning Models?Unnamed ItemLarge Margin Multiclass Gaussian Classification with Differential PrivacyBounding the difference between RankRC and RankSVM and application to multi-level rare class kernel rankingCombinatorial properties of support vectors of separating hyperplanesBlessing of dimensionality: mathematical foundations of the statistical physics of dataA bilateral-truncated-loss based robust support vector machine for classification problemsMulti-parameter regularization and its numerical realizationStochastic Subgradient Estimation Training for Support Vector MachinesOptimization problems in statistical learning: duality and optimality conditionsMixed-norm regularization for brain decodingNonlinear optimization and support vector machinesNonlinear optimization and support vector machinesA sparse large margin semi-supervised learning methodBias of homotopic gradient descent for the hinge lossEfficient approximate leave-one-out cross-validation for kernel logistic regressionAn algebraic characterization of the optimum of regularized kernel methodsThe sparse signomial classification and regression modelIntroduction to Support Vector Machines and Their Applications in Bankruptcy PrognosisAn infeasible-start framework for convex quadratic optimization, with application to constraint-reduced interior-point and other methodsLearning using privileged information: SVM+ and weighted SVMUnnamed ItemIncremental accelerated gradient methods for SVM classification: study of the constrained approach


Uses Software


Cites Work


This page was built for publication: Training a Support Vector Machine in the Primal