A geometrical method to improve performance of the support vector machine
Williams, Peter, Li, Sheng, Feng, Jianfeng and Wu, Si. (2007) A geometrical method to improve performance of the support vector machine. IEEE Transactions on Neural Networks, Vol.18 (No.3). pp. 942-947. ISSN 1045-9227Full text not available from this repository.
Official URL: http://dx.doi.org/10.1109/TNN.2007.891625
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:||Journal Article|
|Subjects:||Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
T Technology > TK Electrical engineering. Electronics Nuclear engineering
|Divisions:||Faculty of Science > Centre for Scientific Computing
Faculty of Science > Computer Science
|Journal or Publication Title:||IEEE Transactions on Neural Networks|
|Official Date:||May 2007|
|Number of Pages:||6|
|Page Range:||pp. 942-947|
|Access rights to Published version:||Restricted or Subscription Access|
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