SSVM: A Amooth Support Vector Machine for Classification
Abstract
Smoothing methods, extensively used for solving important math-
ematical programming problems and applications, are applied here
to generate and solve an unconstrained smooth reformulation of the
support vector machine for pattern classi cation using a completely
arbitrary kernel. We term such reformulation a smooth support vec-
tor machine (SSVM). A fast Newton-Armijo algorithm for solving the
SSVM converges globally and quadratically. Numerical results and
comparisons are given to demonstrate the e ectiveness and speed of
the algorithm. On six publicly available datasets, tenfold cross vali-
dation correctness of SSVM was the highest compared with four other
methods as well as the fastest. On larger problems, SSVM was compa-
rable or faster than SVMlight [17], SOR [23] and SMO [27]. SSVM can
also generate a highly nonlinear separating surface such as a checker-
board.
Subject
smooth support vector machine
Permanent Link
http://digital.library.wisc.edu/1793/64274Citation
99-03