Data Mining via Support Vector Machines
Abstract
Support vector machines (SVMs) have played a key role in broad
classes of problems arising in various elds. Much more recently, SVMs
have become the tool of choice for problems arising in data classi -
cation and mining. This paper emphasizes some recent developments
that the author and his colleagues have contributed to such as: gen-
eralized SVMs (a very general mathematical programming framework
for SVMs), smooth SVMs (a smooth nonlinear equation representation
of SVMs solvable by a fast Newton method), Lagrangian SVMs (an
unconstrained Lagrangian representation of SVMs leading to an ex-
tremely simple iterative scheme capable of solving classi cation prob-
lems with millions of points) and reduced SVMs (a rectangular kernel
classi er that utilizes as little as 1% of the data).
Subject
data classification
support vector machines
Permanent Link
http://digital.library.wisc.edu/1793/64302Citation
01-05