%0 Journal Article
	%A Yi-Fan Zhu and  Wei Zhang and  Xuan Zhou and  Qun Li and  Yong-Lin Lei
	%D 2011
	%J International Journal of Computer and Information Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 57, 2011
	%T Approximation Incremental Training Algorithm Based on a Changeable Training Set
	%U https://publications.waset.org/pdf/5883
	%V 57
	%X The quick training algorithms and accurate solution
procedure for incremental learning aim at improving the efficiency of
training of SVR, whereas there are some disadvantages for them, i.e.
the nonconvergence of the formers for changeable training set and
the inefficiency of the latter for a massive dataset. In order to handle
the problems, a new training algorithm for a changeable training
set, named Approximation Incremental Training Algorithm (AITA),
was proposed. This paper explored the reason of nonconvergence
theoretically and discussed the realization of AITA, and finally
demonstrated the benefits of AITA both on precision and efficiency.
	%P 1026 - 1035