Support vector machine (SVM) which was originally designed for binary classification has achieved superior performance in various classification problems. In order to extend it to ...
We propose a novel privacy-preserving nonlinear support vector machine (SVM) classifier for a data matrix A whose columns represent input space features and whose individual rows ...
We propose a novel privacy-preserving support vector machine (SVM) classifier for a data matrix A whose input feature columns are divided into groups belonging to different entiti...
The decision functions constructed by support vector machines (SVM’s) usually depend only on a subset of the training set—the so-called support vectors. We derive asymptotical...
Kernels are two-placed functions that can be interpreted as inner products in some Hilbert space. It is this property which makes kernels predestinated to carry linear models of l...