DocumentCode
2085189
Title
Support vector machines with continued fraction kernel
Author
Tan, JingDong ; Wang, Rujing ; Zhang, Xiaoming
Author_Institution
Inst. of Intell. Machines, Chinese Acad. of Sci., Hefei, China
Volume
1
fYear
2008
fDate
17-19 Nov. 2008
Firstpage
963
Lastpage
967
Abstract
Based on the proof of a series of Theorems, this paper presents a new continued fraction Mercer kernel, which can be used in SVC algorithm and other SVM algorithm. Experimental results show the SVC algorithm with continued fraction kernel works successfully on real data, and is competitive to the other existing simple kernels. Moreover, this kernel can be used to combine relatively complex kernels such as RBF applying kernel tricks easily.
Keywords
support vector machines; SVC algorithm; continued fraction kernel; machine learning method; statistical learning theory; support vector machines; Helium; Intelligent systems; Kernel; Knowledge engineering; Learning systems; Machine intelligence; Machine learning algorithms; Polynomials; Static VAr compensators; Support vector machines;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent System and Knowledge Engineering, 2008. ISKE 2008. 3rd International Conference on
Conference_Location
Xiamen
Print_ISBN
978-1-4244-2196-1
Electronic_ISBN
978-1-4244-2197-8
Type
conf
DOI
10.1109/ISKE.2008.4731068
Filename
4731068
Link To Document