Sung-Hae Jun and Kyung-Whan Oh An Evolutionary Statistical Learning Theory
3873 - 3880
2007
1
12
International Journal of Computer, Electrical, Automation, Control and Information Engineering http://waset.org/publications/5739
http://waset.org/publications/12
World Academy of Science, Engineering and Technology
Statistical learning theory was developed by Vapnik. It
is a learning theory based on VapnikChervonenkis dimension. It also
has been used in learning models as good analytical tools. In general, a
learning theory has had several problems. Some of them are local
optima and overfitting problems. As well, statistical learning theory
has same problems because the kernel type, kernel parameters, and
regularization constant C are determined subjectively by the art of
researchers. So, we propose an evolutionary statistical learning theory
to settle the problems of original statistical learning theory.
Combining evolutionary computing into statistical learning theory,
our theory is constructed. We verify improved performances of an
evolutionary statistical learning theory using data sets from KDD cup.
International Science Index 12, 2007