Support vector machines based on hybrid kernel function

Gen Ting Yan; Guang Fu Ma; Yu Zhi Xiao

Support vector machines based on hybrid kernel function

Gen Ting Yan^*
, Guang Fu Ma
, Yu Zhi Xiao

^*Corresponding author for this work

School of Astronautics

Harbin Institute of Technology
Shanghai Institute of Aerospace System Engineering

Research output: Contribution to journal › Article › peer-review

13 Scopus citations

Abstract

A support vector machines algorithm based on hybrid kernel function is proposed in this paper. Firstly, it is proofed that the nonnegative combination of common kernels is also a Mercer kernel. And then the nonnegative combination coefficients and hyperparameters of the hybrid kernel are determined via minimizing the RM bound of support vector machines with squared cost function. Simulating experiment results show the effectiveness and efficiency of the presented method.

Original language	English
Pages (from-to)	1704-1706
Number of pages	3
Journal	Harbin Gongye Daxue Xuebao/Journal of Harbin Institute of Technology
Volume	39
Issue number	11
State	Published - Nov 2007

Keywords

Hybrid kernel function
Mercer condition
Radius margin bound
Support vector machines

Cite this

@article{cedc327474824cb68721b29d0fe7c366,

title = "Support vector machines based on hybrid kernel function",

abstract = "A support vector machines algorithm based on hybrid kernel function is proposed in this paper. Firstly, it is proofed that the nonnegative combination of common kernels is also a Mercer kernel. And then the nonnegative combination coefficients and hyperparameters of the hybrid kernel are determined via minimizing the RM bound of support vector machines with squared cost function. Simulating experiment results show the effectiveness and efficiency of the presented method.",

keywords = "Hybrid kernel function, Mercer condition, Radius margin bound, Support vector machines",

author = "Yan, \{Gen Ting\} and Ma, \{Guang Fu\} and Xiao, \{Yu Zhi\}",

year = "2007",

month = nov,

language = "英语",

volume = "39",

pages = "1704--1706",

journal = "Harbin Gongye Daxue Xuebao/Journal of Harbin Institute of Technology",

issn = "0367-6234",

publisher = "Harbin Institute of Technology",

number = "11",

}

TY - JOUR

T1 - Support vector machines based on hybrid kernel function

AU - Yan, Gen Ting

AU - Ma, Guang Fu

AU - Xiao, Yu Zhi

PY - 2007/11

Y1 - 2007/11

N2 - A support vector machines algorithm based on hybrid kernel function is proposed in this paper. Firstly, it is proofed that the nonnegative combination of common kernels is also a Mercer kernel. And then the nonnegative combination coefficients and hyperparameters of the hybrid kernel are determined via minimizing the RM bound of support vector machines with squared cost function. Simulating experiment results show the effectiveness and efficiency of the presented method.

AB - A support vector machines algorithm based on hybrid kernel function is proposed in this paper. Firstly, it is proofed that the nonnegative combination of common kernels is also a Mercer kernel. And then the nonnegative combination coefficients and hyperparameters of the hybrid kernel are determined via minimizing the RM bound of support vector machines with squared cost function. Simulating experiment results show the effectiveness and efficiency of the presented method.

KW - Hybrid kernel function

KW - Mercer condition

KW - Radius margin bound

KW - Support vector machines

UR - https://www.scopus.com/pages/publications/37349016524

M3 - 文章

AN - SCOPUS:37349016524

SN - 0367-6234

VL - 39

SP - 1704

EP - 1706

JO - Harbin Gongye Daxue Xuebao/Journal of Harbin Institute of Technology

JF - Harbin Gongye Daxue Xuebao/Journal of Harbin Institute of Technology

IS - 11

ER -

Support vector machines based on hybrid kernel function

Abstract

Keywords

Other files and links

Fingerprint

Cite this