Competitor Recognition Based on Improved SVM Algorithm in E-commerce
Journal Title: 河南科技大学学报（自然科学版） - Year 2016, Vol 37, Issue 1
Abstract
To solve the low accuracy of identification of potential competitors in electronic commerce,an improved( support vector machine,SVM) algorithm was presented to optimize the recognition. By combining the Mercer theorem with the factor of margin,the kernel function was modified step by step in order to improve the generalization ability of classifier. Taking Listing information extracted from ebay as the object of research,the kernel function was transformed through conformal mapping based on the Mercer theorem firstly. Then based on the Riemannian geometry theory,the kernel function was improved by integrating the interval factors to form a new classifier. Finally the parameter was chosen automatically to determine the model and test the data. Compared with vector similarity algorithm and the original SVM algorithm,the accuracy of improved algorithm to identify competitors is increased by 10. 2% and 3. 8% respectively.
Authors and Affiliations
Ruili SUN, Shengshuang CHEN, Shijun LI
Keywords
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- EP ID EP459951
- DOI 10.15926/j.cnki.issn1672-6871.2016.01.010
- Views 77
- Downloads 0
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