A Complete and Improved Mathematical Framework of the Geometric Approach Based on the Scaled Convex Hull (SCH) and its Application to Large Medical Data Sets

Hao Yan Guo; Yuan Zhi Cheng; Da Zheng Wang; Jia Cheng Xu

doi:10.4028/www.scientific.net/AMM.556-562.3935

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 556-562A Complete and Improved Mathematical Framework of...

A Complete and Improved Mathematical Framework of the Geometric Approach Based on the Scaled Convex Hull (SCH) and its Application to Large Medical Data Sets

Abstract:

In this paper a complete and improved mathematical framework of the geometric approach based on the Scaled convex hull (SCH), which includes two parts, SCH-based geometric algorithms for SVM and a fast and novel geometric method for model selection in SCH-based SVM, is developed to solve SCH-based SVM classification of large data sets. On the basis of this framework, these geometric algorithms are more suitable for classification task of large data sets. Results of numerical experiments show that the proposed geometric algorithms can reduce kernel calculations and display nice performances on large medical data sets, such as computer assisted screening for lung cancer.

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Periodical:

Applied Mechanics and Materials (Volumes 556-562)

Pages:

3935-3940

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.556-562.3935

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Online since:

May 2014

Authors:

Hao Yan Guo*, Yuan Zhi Cheng, Da Zheng Wang, Jia Cheng Xu

Keywords:

Geometric Algorithm, Kernel Methods, Scaled Convex Hull (SCH), Support Vector Machine (SVM)

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* - Corresponding Author

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