Now showing items 1-6 of 6
Privacy-Preserving Linear and Nonlinear Approximation via Linear Programming
We propose a novel privacy-preserving random kernel approximation based on a data matrix A ? Rm�n whose rows are divided into privately owned blocks. Each block of rows belongs to a different entity that is unwilling to ...
Support Vector Machine Classi cation via Parameterless Robust Linear Programming
We show that the problem of minimizing the sum of arbitrary-norm real distances to misclassi ed points, from a pair of parallel bounding planes of a classi cation problem, divided by the margin (distance) be- tween the ...
Large Scale Kernel Regression via Linear Programming
The problem of tolerant data tting by a nonlinear surface, in- duced by a kernel-based support vector machine , is formulated as a linear program with fewer number of variables than that of other linear programming ...
Knowledge-Based Support Vector Machine Classi ers
Prior knowledge in the form of multiple polyhedral sets, each belonging to one of two categories, is introduced into a reformulation of a linear support vector machine classi er. The resulting formulation leads to a ...
Data Selection for Support Vector Machine Classifiers
The problem of extracting a minimal number of data points from a large dataset, in order to generate a support vector machine (SVM) classi er, is formulated as a concave minimization problem and solved by a nite number ...
Knowledge-Based Nonlinear Kernel Classi ers
Prior knowledge in the form of multiple polyhedral sets, each belonging to one of two categories, is introduced into a reformulation of a nonlinear kernel support vector machine (SVM) classi er. The resulting formulation ...