A tutorial on Support Vector Machines for pattern recognition
A tutorial on Support Vector Machines for pattern recognition
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DOI：
10.1023/a:1009715923555
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发表时间：
1998-06-01
影响因子：
4.8
通讯作者：
Burges, CJC
中科院分区：
文献类型：
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作者：
Burges, CJC
教程以VC维度和结构风险最小化的概念概述开始。然后，我们为可分离和不可分割的数据描述线性支持向量机（SVM），详细介绍了非平凡示例。我们描述了一个机械类比，并讨论何时SVM解决方案是唯一的，何时它们是全球的。我们描述了如何实际实施支持向量培训，并详细讨论了用于构建数据中非线性的SVM解决方案的内核映射技术。我们通过计算均质多项式和高斯径向基函数核的VC维度来展示支持向量机如何具有非常大的（甚至是无限）VC维度。尽管很高的VC维度通常会因泛化性能而受到验证，而目前尚无理论表明，SVM可以保证良好的概括性能，但有几个参数支持观察到的SVM的高精度，我们会审查。还提出了一些受这些论点启发的实验的结果。我们给出了大多数关键定理的许多例子和证明。有新的材料，我希望读者能发现即使是旧材料也被新鲜的材料铸造。
The tutorial starts with an overview of the concepts of VC dimension and structural risk minimization. We then describe linear Support Vector Machines (SVMs) for separable and non-separable data, working through a non-trivial example in detail. We describe a mechanical analogy, and discuss when SVM solutions are unique and when they are global. We describe how support vector training can be practically implemented, and discuss in detail the kernel mapping technique which is used to construct SVM solutions which are nonlinear in the data. We show how Support Vector machines can have very large (even infinite) VC dimension by computing the VC dimension for homogeneous polynomial and Gaussian radial basis function kernels. While very high VC dimension would normally bode ill for generalization performance, and while at present there exists no theory which shows that good generalization performance is guaranteed for SVMs, there are several arguments which support the observed high accuracy of SVMs, which we review. Results of some experiments which were inspired by these arguments are also presented. We give numerous examples and proofs of most of the key theorems. There is new material, and I hope that the reader will find that even old material is cast in a fresh light.