摘要
文章比较了两种学习机器:径向基函数、具有高斯核函数的支撑矢量机(SVM)。试验表明SVM能够获得最高的正确识别率。因此,支撑矢量机不只很好地建立在理论上,而且应用时也具有很好的优越性。
The support vector machine(SVM) is a new type of learning machine, based on statistical learning theory, which contains polynomial classifiers, neural networks, and radial basis function (RBF). In the RBF case, the SVM algorithm automatically determines centers, weights, and threshold that minimize an upper bound on the expected test error. The present study is devoted to an experimental comparison of the two machines: a RBF classifier, an SVM with Gaussian kernel. Our results show that the SVM achieves the highest recognition accuracy, followed by the hybrid system. The SVM approach is thus not only theoretically well-founded but also superior in a practical application.
出处
《交通与计算机》
2003年第3期44-48,共5页
Computer and Communications