Earticle

초록

영어

According to the merits and shortcomings of the traditional gridsearch algorithm in parameters optimization of support vector machine (SVM), an improved grid search algorithm is proposed. Dichotomous search algorithm is used to reduce target searching range. First, searching range is determined roughly, and a set of parameters are obtained. Then fine search is applied in reduction the range for searching, and searching the optimum parameters.Three kinds of famous tumor gene data set are used in the comparison experiments to validate the classification accuracy of principal component analysis (PCA)-SVM and kernel principal component analysis (KPCA)-SVM. Experiment results and data analysis shows that, comparing with traditional gridsearch algorithm, the proposed method has higher classification accuracy and less search time.

목차

Abstract
 1. Introduction
 2. SVM
 3. The Improved Grid Search Algorithm
 4. Experimental Results
 5. Conclusion
 Acknowledgement
 References

키워드

저자

• Li Wenwen [ College of Communications Engineering, Jilin University, Changchun 130022, China, College of Mechanical Engineering, Baicheng Normal University, Baicheng 137000, China ]
• Xing Xiaoxue [ College of Communications Engineering, Jilin University, Changchun 130022, China, Collegeof Information Engineering, Changchun University, Changchun 130022, China ]
• Liu Fu [ College of Communications Engineering, Jilin University, Changchun 130022, China ] Corresponding Author
• Zhang Yu [ College of Communications Engineering, Jilin University, Changchun 130022, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605