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Now showing items 1-6 of 6

#### Privacy-Preserving Linear and Nonlinear Approximation via Linear Programming

(2011)

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

(2003)

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

(1999)

The problem of tolerant data tting by a nonlinear surface, in-
duced by a kernel-based support vector machine [24], is formulated as
a linear program with fewer number of variables than that of other
linear programming ...

#### Knowledge-Based Support Vector Machine Classi ers

(2001)

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

(2000)

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

(2003)

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 ...