Abstract
The support vector machine (SVM) parameters optimization of previous lacks of theoretical guidance. The algorithm is easy to fall into local optimum. The dual population ant colony algorithm is helpful to SVM parameters optimization in ability. The independent solution and exchange of two population information and the change of pheromone can avoid the stagnation of the algorithm or get into local optimum, and find the global optimal solution. The accuracy of SVM classification is used as the objective function. And the experimental results show that the algorithm improves the accuracy of the classification of support vector machine parameters.
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 callDisclaimer: 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.
