A geometric method to improve the performance of the support vector machine

Williams, Peter, Li, Sheng, Feng, Jianfeng and Wu, Si (2007) A geometric method to improve the performance of the support vector machine. IEEE Transactions on Neural Networks, 18 (3). pp. 942-947. ISSN 1045-9227

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The performance of a support vector machine (SVM) largely depends on the kernel function used. This letter investigates a geometrical method to optimize the kernel function. The method is a modification of the one proposed by S. Amari and S. Wu. Its concern is the use of the prior knowledge obtained in a primary step training to conformally rescale the kernel function, so that the separation between the two classes of data is enlarged. The result is that the new algorithm works efficiently and overcomes the susceptibility of the original method

Item Type: Article
Additional Information: Originality: Significantly improved a well-known method in machine learning for optimizing the kernel function of support vector machines, the current state-of-art pattern recognition method. Rigor: Applied the Information Geometry method to analyze the behaviours of kernel mapping. The new method was tested by real-world problem. Significance: this new method significantly improves the performance of the previous one, and makes this type of geometry-based method more robust and easier to use. Outlet/Citation: Top Engineering/Machine Learning journal. In press.
Schools and Departments: School of Engineering and Informatics > Informatics
Depositing User: Si Wu
Date Deposited: 20 Feb 2013 10:50
Last Modified: 20 Feb 2013 10:50
URI: http://srodev.sussex.ac.uk/id/eprint/30303
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