Now showing items 1-5 of 5
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 ...
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 ...
Equivalence of Minimal L0 and Lp Norm Solutions of Linear Equalities, Inequalities and Linear Programs for Sufficiently Small p
For a bounded system of linear equalities and inequalities we show that the NP-hard ?0 norm minimization problem min ||x||0 subject to Ax = a, Bx ? b and ||x||? ? 1, is completely equivalent to the concave minimization ...