LibSVM中的哪个参数是松弛变量? [英] Which of the parameters in LibSVM is the slack variable?

问题描述

我对SVM中的命名有些困惑.我正在使用该库 LibSVM .可以设置的参数太多了.有谁知道松弛变量是哪一个?

I am a bit confused about the namings in the SVM. I am using this library LibSVM. There are so many parameters that can be set. Does anyone know which of these is the slack variable? thx

推荐答案

松弛变量"在c-svm中为C，在nu-SVM中为nu.它们在各自的公式中起着相同的作用-控制宽边距和分类器误差之间的折衷.在C的情况下，通常以10 ^ -4、10 ^ -3、10 ^ -2，...到1，5左右的数量级对其进行测试. nu是一个介于0和1之间的数字，通常是.1到.8，它控制支持向量与数据点的比率.当nu为.1时，边距较小，支持向量的数量将占数据点数量的一小部分.当nu为.8时，边距非常大，大多数点将落在边距中.

The "slack variable" is C in c-svm and nu in nu-SVM. These both serve the same function in their respective formulations - controlling the tradeoff between a wide margin and classifier error. In the case of C, one generally test it in orders of magnitude, say 10^-4, 10^-3, 10^-2,... to 1, 5 or so. nu is a number between 0 and 1, generally from .1 to .8, which controls the ratio of support vectors to data points. When nu is .1, the margin is small, the number of support vectors will be a small percentage of the number of data points. When nu is .8, the margin is very large and most of the points will fall in the margin.

要考虑的其他事项是您选择内核(线性，RBF，Sigmoid，多项式)和所选内核的参数.通常，必须做大量的实验才能找到最佳的参数组合.但是，请注意不要过度适合您的数据集.

The other things to consider are your choice of kernel (linear, RBF, sigmoid, polynomial) and the parameters for the chosen kernel. Generally one has to do a lot of experimenting to find the best combination of parameters. However, be careful of over-fitting to your dataset.

Burges写了一篇很棒的教程:用于模式的支持向量机教程 识别

Burges wrote a great tutorial: A Tutorial on Support Vector Machines for Pattern Recognition

但是，如果您只想知道如何使用它，而不太了解它的工作原理，请阅读

But if you mostly just want to know how to USE it and less about how it works, read "A Practical Guide to Support Vector Classication" by Chih-Wei Hsu, Chih-Chung Chang, and Chih-Jen Lin (authors of libsvm)

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