DocumentCode
2737960
Title
Non-linear pattern recognition based on SVM and genetic algorithm
Author
Jingfang, Wang
Author_Institution
Dept. of Electr. Eng., Hunan Int. Econ. Univ., Changsha, China
fYear
2011
fDate
21-23 Oct. 2011
Firstpage
694
Lastpage
698
Abstract
This paper presents a support vector machine (SVM) model structure, the genetic algorithm parameters of the model portfolio optimization model, and used for non-linear pattern recognition. The method is not only effective for linear problems, nonlinear problems application and simple and easy, but also proves better than the multi-segment linear classifier design methods and BP network algorithm that returns with errors. Examples show the efficiency of 100% recognition.
Keywords
backpropagation; genetic algorithms; pattern classification; support vector machines; BP network algorithm; VM model structure; genetic algorithm parameters; linear problem; model portfolio optimization model; multisegment linear classifier design method; nonlinear pattern recognition; nonlinear problem; support vector machine; Aerospace electronics; Biological cells; Genetic algorithms; Kernel; Optimization; Pattern recognition; Support vector machines; combinatorial optimization; genetic algorithm; nonlinear; pattern recognition; support vector machine;
fLanguage
English
Publisher
ieee
Conference_Titel
Image Analysis and Signal Processing (IASP), 2011 International Conference on
Conference_Location
Hubei
Print_ISBN
978-1-61284-879-2
Type
conf
DOI
10.1109/IASP.2011.6109137
Filename
6109137
Link To Document