Adaptive Kernel Scaling Support Vector Machine

  • 报告人: Wenqing HeProfessor
  • 报告人单位: University of Western Ontario
  • 报告地址: 数学学院409报告厅
  • 报告时间: 2017年8月31日(星期四)下午16:00
  • 主题摘要:
Support Vector Machine (SVM) is popularly used in the classification/prediction of discrete outcomes, especially in high dimensional data analysis such as gene expression data analysis and image analysis. In this talk, a new enhanced SVM method will be presented. The initial kernel function for the SVM is rescaled in an adaptive fashion so that the separation between two classes can be effectively enlarged, based on the prior knowledge obtained from the conventional SVM. The modified classifier takes into consideration of the location of the support vectors in the feature space. Improvement of prediction accuracy from this data dependent SVM is shown with numerical studies, and a prostate cancer image data is analyzed as an illustration.