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Lagrangian Support Vector Machines
(2000)
An implicit Lagrangian for the dual of a simple reformulation of
the standard quadratic program of a linear support vector machine
is proposed. This leads to the minimization of an unconstrained
di erentiable convex ...
Multiple Instance Classification via Successive Linear Programming
(2005)
The multiple instance classification problem [6,2,12] is formulated using a linear
or nonlinear kernel as the minimization of a linear function in a finite dimensional
(noninteger) real space subject to linear and bilinear ...
Massive Data Classification via Unconstrained Support Vector Machines
(2006)
A highly accurate algorithm, based on support vector machines
formulated as linear programs [13, 1], is proposed
here as a completely unconstrained minimization problem
[15]. Combined with a chunking procedure [2] this ...
Slice Models in General Purpose Modeling Systems
(2000-12-14)
Slice models are collections of mathematical programs with the same
structure but di erent data. Examples of slice models appear in Data
Envelopment Analysis, where they are used to evaluate e ciency, and
cross-validation, ...
Finite Newton Method for Lagrangian Support Vector Machine Classi cation
(2002)
An implicit Lagrangian [19] formulation of a support vector machine
classi er that led to a highly e ective iterative scheme [18] is
solved here by a nite Newton method. The proposed method, which
is extremely fast and ...
RSVM: Reduced Support Vector Machines
(2001-01)
An algorithm is proposed which generates a nonlinear kernel-based
separating surface that requires as little as 1% of a large dataset for its explicit
evaluation. To generate this nonlinear surface, the entire dataset ...
Exactness Conditions for a Convex Differentiable Exterior Penalty for Linear Programming
(2007)
Sufficient conditions are given for a classical dual exterior penalty
function of a linear program to be independent of its penalty parameter.
This ensures that an exact solution to the primal linear program can be
obtained ...
Feature Selection in k-Median Clustering
(2004)
An e ective method for selecting features in clustering
unlabeled data is proposed based on changing the objective
function of the standard k-median clustering algorithm. The
change consists of perturbing the objective ...
Incremental Support Vector Machine Classi cation
(2001)
Using a recently introduced proximal support vector ma-
chine classi er [4], a very fast and simple incremental support vector
machine (SVM) classi er is proposed which is capable of modifying an
existing linear classi ...
An optimization approach for radiosurgery treatment planning
(2001-11-06)
We outline a new approach for radiosurgery treatment planning, based
on solving a series of optimization problems. We consider a speci c treat-
ment planning problem for a specialized device known as the Gamma
Knife, ...










