@article{(Open Science Index):https://publications.waset.org/pdf/5739,
	  title     = {An Evolutionary Statistical Learning Theory},
	  author    = {Sung-Hae Jun and  Kyung-Whan Oh},
	  country	= {},
	  institution	= {},
	  abstract     = {Statistical learning theory was developed by Vapnik. It
is a learning theory based on Vapnik-Chervonenkis 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 over-fitting 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.},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {1},
	  number    = {12},
	  year      = {2007},
	  pages     = {3873 - 3880},
	  ee        = {https://publications.waset.org/pdf/5739},
	  url   	= {https://publications.waset.org/vol/12},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 12, 2007},