Title :
A Geometrical Method to Improve Performance of the Support Vector Machine
Author :
Williams, P. ; Sheng Li ; Jianfeng Feng ; Si Wu
Author_Institution :
Dept. of Informatics, Sussex Univ., Brighton
fDate :
5/1/2007 12:00:00 AM
Abstract :
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
Keywords :
geometry; support vector machines; geometrical method; kernel function; support vector machine; Artificial neural networks; Backpropagation algorithms; Distributed processing; Kernel; Machine learning; Multidimensional systems; Neural networks; Optimization methods; Support vector machines; Testing; Classification; Riemannian goemetry; conformal transformation; kernel function; support vector machine (SVM); Algorithms; Artificial Intelligence; Computer Simulation; Decision Support Techniques; Information Storage and Retrieval; Models, Theoretical; Neural Networks (Computer); Pattern Recognition, Automated;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2007.891625
