Now showing items 41-46 of 46
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 ...
Privacy-Preserving Random Kernel Classification of Checkerboard Partitioned Data
We propose a privacy-preserving support vector machine (SVM) classifier for a data matrix A whose input feature columns as well as individual data point rows are divided into groups belonging to different entities. Each ...
Breast Tumor Susceptibility to Chemotherapy via Support Vector Machines
Support vector machines (SVMs), utilizing RNA signature measurements, were used to generate a classi er to distinguish breast cancer patients that are partial-responders to chemotherapy treatment, from patients that are ...
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 ...
Absolute Value Equation Solution via Dual Complementarity
By utilizing a dual complementarity condition, we propose an iterative method for solving the NPhard absolute value equation (AVE): Ax?|x| = b, where A is an n�n square matrix. The algorithm makes no assumptions on the ...