一般性的是C-SVC。nu-SVC
见:http://scikit-learn.org/stable/modules/svm.html#svm-mathematical-formulation
https://www.quora.com/What-is-the-difference-between-C-SVM-and-nu-SVM
- The nu-SVM was proposed by Scholkopf et al has the advantage of using a parameter nu for controlling the number of support vectors. The parameter C in the ordinary SVM formulation is replaced by a parameter nu which is bounded by 0 and 1. Earlier the parameter C could have taken any positive value, thus this additional bound is beneficial in implementation.
- The parameter nu represents the lower and upper bound on the number of examples that are support vectors and that lie on the wrong side of the hyperplane, respectively.
- Now despite the new bound, the nu-SVM is comparatively difficult to optimize and often the runtime is not scalable as compared to C-SVM.
https://www.csie.ntu.edu.tw/~cjlin/libsvm/faq.html#f411：
- Q: What is the difference between nu-SVC and C-SVC?
- Basically they are the same thing but with different parameters. The range of C is from zero to infinity but nu is always between [0,1]. A nice property of nu is that it is related to the ratio of support vectors and the ratio of the training error.
参考连接：http://blog.sina.com.cn/s/blog_6e32babb0102xpip.html
https://www.csie.ntu.edu.tw/~cjlin/libsvm/faq.html#f411
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