The effectiveness of kernelized support vector machine in classification depends on
the choice of kernel function, kernel parameter and soft margin parameter. In practice, there is
need for proper guidance on the combination of kernel functions and soft margin parameters to be
used. An insight into this is provided in this study. In this paper, we explore the notion of support
vector machine and its kernelized version, investigate the performance of some kernel functions
and soft margin parameters in support vector classification for some training sample sizes in .
We also examine the performance of kernelized support vector machine in functional setting and
compare the classifier with maximum functional depth classification methods and centroid classifier
based on simulation.
Keywords: Support vection machine, kernel functions, soft margin parameters, classification, error
rates
PAPER TITLE :ON SOME KERNEL BASED SUPPORT VECTOR MACHINES
JOURNAL Of SUSTAINABLE TECHNOLOGY | VOLUME 9 NUMBER 2 2018
Paper Details
- Author(s) : MAKINDE, O.S.* and BODUNWA, O.K.
- Abstract:
