Neural Computing and Applications

, Volume 18, Issue 3, pp 275–282 | Cite as

Applying a novel decision rule to the sphere-structured support vector machines algorithm

Original Article


The traditional sphere-structured support vector machines algorithm is one of the learning methods. It can partition the training samples space by means of constructing the spheres with the minimum volume covering all training samples of each pattern class in high-dimensional feature space. However, the decision rule of the traditional sphere-structured support vector machines cannot assign ambiguous sample points such as some encircled by more than two spheres to valid class labels. Therefore, the traditional sphere-structured support vector machines is insufficient for obtaining the better classification performance. In this article, we propose a novel decision rule applied to the traditional sphere-structured support vector machines. This new decision rule significantly improves the performance of labeling ambiguous points. Experimental results of seven real datasets show the traditional sphere-structured support vector machines based on this new decision rule can not only acquire the better classification accuracies than the traditional sphere-structured support vector machines but also achieve the comparable performance to the classical support vector machines.


Pattern classification Sphere-structured support vector machines Decision rule Kernel functions 



We thank the anonymous reviewer’s constructive comments for improving presentation of this article.


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Copyright information

© Springer-Verlag London Limited 2008

Authors and Affiliations

  1. 1.School of Computer Science and Technology, Room 605, Teaching and Research Section of Computer ScienceNanjing University of Science and TechnologyNanjingChina

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