Hidden Space Smooth Support Vector Machine with C Means Clustering

DOI

10.2991/icmit-16.2016.159How to use a DOI?

Keywords

Hidden Space;Smooth technique;Support Vector Machine;FCM

Abstract

A piecewise smooth model is proposed and called HSSSVM-CM for short in the hidden space. Mapping the training data to the hidden space with a hidden function, HSSSVM-CM divides the original data into several subclasses by C means; derives the smooth differentiable unconstrained model by utilizing the entropy function to approximate the plus function of the slack vector, and introduces linking rules to combine classification results of various subclasses. Simulations on benchmark data demonstrate that HSSSVM-CM maintains good classification accuracies, reduces the training time and hardly varies with kernel parameters.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Jinjin Liang
AU  - Wenhao Xie
AU  - Xiaoyan Wang
PY  - 2016/04
DA  - 2016/04
TI  - Hidden Space Smooth Support Vector Machine with C Means Clustering
BT  - Proceedings of the 2016 3rd International Conference on Mechatronics and Information Technology
PB  - Atlantis Press
SP  - 881
EP  - 886
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmit-16.2016.159
DO  - 10.2991/icmit-16.2016.159
ID  - Liang2016/04
ER  -