A class possibility based kernel to increase classification accuracy for small data sets using support vector machines

Der Chiang Li, Chiao Wen Liu

研究成果: Article同行評審

52 引文 斯高帕斯(Scopus)

摘要

Appropriate choice of kernels is the most important task when using kernel-based learning methods such as support vector machines. The current widely used kernels (such as polynomial kernel, Gaussian kernel, two-layer perceptron kernel, and so on) are all functional kernels for general purposes. Currently, there is no kernel proposed in a data-driven way. This paper proposes a new kernel generating method dependent on classifying related properties of the data structure itself. The new kernel concentrates on the similarity of paired data in classes, where the calculation of similarity is based on fuzzy theories. The experimental results with four medical data sets show that the proposed kernel has superior classification performance than polynomial and Gaussian kernels.

原文English
頁(從 - 到)3104-3110
頁數7
期刊Expert Systems With Applications
37
發行號4
DOIs
出版狀態Published - 2010 四月 1

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

指紋 深入研究「A class possibility based kernel to increase classification accuracy for small data sets using support vector machines」主題。共同形成了獨特的指紋。

引用此