DocumentCode
1810512
Title
A novel support vector machine kernel based on Slepian semi-wavelets
Author
Shen, Xiaoping
Author_Institution
Dept. of Math., Ohio Univ., Athens, OH, USA
fYear
2011
fDate
20-22 July 2011
Firstpage
65
Lastpage
68
Abstract
In this paper, we construct a positive definite kernel associated with Slepian semi-wavelets. The kernel possesses multiscale structure and exhibits a strong localization property. It is convolution type associated with asymptotic sparse Gram matrix and allows the use of thresholding methods. We then focus on developing practical numerical algorithm to compute the kernel. Applications of the kernel in the context of kernel adaptive filtering are discussed.
Keywords
Hilbert spaces; adaptive filters; convolution; sparse matrices; support vector machines; wavelet transforms; Slepian semiwavelets-based support vector machine kernel; asymptotic sparse Gram matrix; kernel adaptive filtering; localization property; multiscale structure; numerical algorithm; positive definite kernel; thresholding methods; Hilbert space; Kernel; Machine learning; Presses; Uncertainty; Wave functions; Wavelet transforms;
fLanguage
English
Publisher
ieee
Conference_Titel
Aerospace and Electronics Conference (NAECON), Proceedings of the 2011 IEEE National
Conference_Location
Dayton, OH
ISSN
0547-3578
Print_ISBN
978-1-4577-1040-7
Type
conf
DOI
10.1109/NAECON.2011.6183079
Filename
6183079
Link To Document