Classification of Sonar Targets using Support Vector Machine

摘要：

The support vector machine (SVM) is a typical binary classifier having the global optimal solution because it deals with a quadratic optimization problem in the training process. In this paper we apply the SVM with a radial basis function to the classification of sonar targets and evaluate the recognition performance depending on the parameters of the SVM. The active sonar data set was obtained from the UCI machine learning repository. This data set consists of 208 patterns of active sonar returns, 111 of metal cylinder returns and 97 of rock returns having similar shapes. To find the optimum parameters of SVM empirically, we use a grid-search method and performance is evaluated in aspect-angle dependent experiment and aspect-angle independent experiment.

作者: Jeonghyun Park Keunsung Bae Chansik Hwang

作者单位: School of Electrical Engineering and Computer Science Kyungpook National University,Daegu,Korea

会议类型: 国际会议

会议名称: The 10th Western Pacific Acoustics Conference(第十届西太平洋声学会议WESPAC X)

会议地点: 北京

会议语种:英文

页码: 1-6

在线出版日期: 2009-09-21（万方平台首次上网日期，不代表论文的发表时间）