python - scikit 中的 SVC 与 LinearSVC 学习 : difference of loss function

Question

根据 this post ，scikit learn中的SVC和LinearSVC有很大的不同。但是在阅读 official scikit learn documentation 时，不是很清楚。
特别是对于损失函数，似乎有一个等价:

和 this post说 le 损失函数是不同的:

SVC : 1/2||w||^2 + C SUM xi_i

线性SVC:1/2||[w b]||^2 + C SUM xi_i

似乎在 LinearSVC 的情况下，截距是正则化的，但官方文档另有说明。
有人有更多信息吗？谢谢

Answer 1

SVC是 LIBSVM 的包装器图书馆，而 LinearSVC是 LIBLINEAR 的包装器LinearSVC是 一般更快 SVC并且可以处理更大的数据集，但它只能使用线性内核，因此得名。所以区别不在于制定，而在于实现方法。
报价LIBLINEAR FAQ :

When to use LIBLINEAR but not LIBSVM

There are some large data for which with/without nonlinear mappings gives similar performances. 
Without using kernels, one can quickly train a much larger set via a linear classifier. 
Document classification is one such application. 
In the following example (20,242 instances and 47,236 features; available on LIBSVM data sets), 
the cross-validation time is significantly reduced by using LIBLINEAR:

% time libsvm-2.85/svm-train -c 4 -t 0 -e 0.1 -m 800 -v 5 rcv1_train.binary
Cross Validation Accuracy = 96.8136%
345.569s

% time liblinear-1.21/train -c 4 -e 0.1 -v 5 rcv1_train.binary
Cross Validation Accuracy = 97.0161%
2.944s

Warning:While LIBLINEAR's default solver is very fast for document classification, it may be slow in other situations. See Appendix C of our SVM guide about using other solvers in LIBLINEAR.
Warning:If you are a beginner and your data sets are not large, you should consider LIBSVM first.