## Search

Now showing items 1-9 of 9

#### 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 ...

#### 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, ...

#### Optimization of Gamma Knife Radiosurgery

(2000)

The Gamma Knife is a highly specialized treatment unit that pro-
vides an advanced stereotactic approach to the treatment of tumors, vascular
malformations, and pain disorders within the head. Inside a shielded ...

#### Robust Linear and Support Vector Regression

(2000-09)

The robust Huber M-estimator, a differentiable cost function that is quadratic for small errors and linear otherwise, is
modeled exactly, in the original primal space of the problem, by an easily solvable simple convex ...

#### Interior Point Methods for Massive Support Vector Machines

(2000-05-25)

We investigate the use of interior point methods for solving quadratic
programming problems with a small number of linear constraints where
the quadratic term consists of a low-rank update to a positive semi-de nite
matrix. ...

#### FATCOP 2.0: Advanced Features in an Opportunistic Mixed Integer Programming Solver

(2000)

We describe FATCOP 2.0, a new parallel mixed integer program solver that
works in an opportunistic computing environment provided by the Condor resource
management system. We outline changes to the search strategy of ...

#### A Practical Approach to Sample-path Simulation Optimization

(2000)

We propose solving continuous parametric simulation
optimizations using a deterministic nonlinear optimiza-
tion algorithm and sample-path simulations. The op-
timization problem is written in a modeling language
with ...

#### Semismooth Support Vector Machines

(2000-11-29)

The linear support vector machine can be posed as a quadratic pro-
gram in a variety of ways. In this paper, we look at a formulation using
the two-norm for the misclassi cation error that leads to a positive de -
nite ...

#### 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 ...