Related papers: Comment on "Support Vector Machines with Applicati…

We review the concept of Support Vector Machines (SVMs) and discuss examples of their use in a number of scenarios. Several SVM implementations have been used in HEP and we exemplify this algorithm using the Toolkit for Multivariate…

Contributed discussion and rejoinder to "Geodesic Monte Carlo on Embedded Manifolds" (arXiv:1301.6064)

We have made initial studies of the potential of support vector machines (SVM) for providing statistical models of nuclear systematics with demonstrable predictive power. Using SVM regression and classification procedures, we have created…

Comment on Causal Inference in the Medical Area [math.ST/0612783]

Comment on ``On Random Scan Gibbs Samplers'' [arXiv:0808.3852]

Comment on ``Performance of Double-Robust Estimators When ``Inverse Probability'' Weights Are Highly Variable'' [arXiv:0804.2958]

Advances in statistical learning theory present the opportunity to develop statistical models of quantum many-body systems exhibiting remarkable predictive power. The potential of such ``theory-thin'' approaches is illustrated with the…

Using an alternate description of support varieties of pairs of modules over a complete intersection, we give several new applications of such varieties, including results for support varieties of intermediate complete intersections.…

Comment on ``Microarrays, Empirical Bayes and the Two-Groups Model'' [arXiv:0808.0572]

Comment on ``Microarrays, Empirical Bayes and the Two-Groups Model'' [arXiv:0808.0572]

Comment on "Classical Simulations Including Electron Correlations for Sequential Double Ionization" [arXiv:1204.3956]

Memoir on the Sigma invariants and their applications, version 2

Isoparametric hypersurfaces and their application to special geometries

Comment on ``Microarrays, Empirical Bayes and the Two-Group Model'' [arXiv:0808.0572]

Presentation of set matrices and demonstration of their efficiency as a tool using the path/cycle problem.

Subgradient algorithms for training support vector machines have been quite successful for solving large-scale and online learning problems. However, they have been restricted to linear kernels and strongly convex formulations. This paper…

An importance sampling and bagging approach to solving the support vector machine (SVM) problem in the context of large databases is presented and evaluated. Our algorithm builds on the nearest neighbors ideas presented in Camelo at al.…

The support vector machine (SVM) algorithm is well known to the computer learning community for its very good practical results. The goal of the present paper is to study this algorithm from a statistical perspective, using tools of…

Rejoinder to ``Boosting Algorithms: Regularization, Prediction and Model Fitting'' [arXiv:0804.2752]

Reply to Comment on 'Length Scale Dependence of DNA Mechanical Properties'