Refined rademacher chaos complexity bounds with applications to the multikernel learning problem.
Estimating the Rademacher chaos complexity of order two is important for understanding the performance of multikernel learning (MKL) machines. In this letter, we develop a novel entropy integral for Rademacher chaos complexities. As compared to the previous bounds, our result is much improved in that it introduces an adjustable parameter ε to prohibit the divergence of the involved integral. With the use of the iteration technique in Steinwart and Scovel (2007), we also apply our Rademacher chaos complexity bound to the MKL problems and improve existing learning rates.
State Key Lab of Software Engineering, School of Computer, Wuhan University, Wuhan 430072, China ywlei@whu.edu.cn.
Yunwen
Y
Lei
Lixin
L
Ding
D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D007858:Learning / Q000502:physiology*; D017711:Nonlinear Dynamics; D010363:Pattern Recognition, Automated