Abstract
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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Multimedia and Ubiquitous Engineering
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
