discriminant analysis (LDA) under the normal setting, we contrast such algorithmic methods as the support vector machine (SVM) and … 60% for the SVM and 50% to 80% for boosting when compared to the LDA. However, a smooth variant of the SVM is shown to be … experiments under various settings for comparisons of finite-sample performance and robustness to mislabeling and model …

kernel methods to estimate the conditional density function in an additive error model, when the error distribution is known …

uniform consistency results for our estimators. …

method of smoothing the DM with a kernel function and using it in estimation. It is seen that smoothing allows us to develop …

-MBR.texon of kernel density estimates over general connected compact sets. The theoretical validity of this approximation is also …

In nonparametric classification and regression problems, regularized kernel methods, in particular support vector … machines, attract much attention in theoretical and in applied statistics. In an abstract sense, regularized kernel methods …) reproducing kernel Hilbert space. For smooth loss functions L, it is shown that the difference between the estimator, i …

on reproducing kernel Hilbert spaces (RKHS) under the multivariate correlated response setup. This provides a full … probabilistic description of support vector machine (SVM) rather than an algorithm for fitting purposes. We have also introduced a … Monte Carlo technique for computation. We have also proposed an empirical Bayes method for our RVM and SVM. Our methods are …

selection consistency, that is, they can asymptotically pick out the true model. Simulation studies show that the proposed …