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具有高斯核函数的支撑矢量机与径向基函数分类器的比较

The Comparison between SVM with Gaussian Kernel and Radial Basis Function Classifier
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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.
机构地区 长安大学
出处 《交通与计算机》 2003年第3期44-48,共5页 Computer and Communications
关键词 高斯核函数 支撑矢量机 径向基函数网络 统计学习理论 网络结构 学习策略 结构风险最小化 广义最优分类面 分类机理 radial basis function networks statistical learning theory structural risk minimization support vector machine
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参考文献7

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