scikit中的SVC与LinearSVC学习：损失函数的差异

问题描述

根据this post，scikit学习中的SVC和LinearSVC非常不同。但是当阅读official scikit learn documentation时，并不清楚。

特别是对于损失函数，似乎存在一个等效项：

this post说le损失函数是不同的：

• SVC：1/2||w||^2 + C SUM xi_i
• LinearSVC：1/2||[w b]||^2 + C SUM xi_i

在LinearSVC的情况下，截距似乎是正则化的，但官方文档中则相反。

有人有更多信息吗？谢谢

解决方法

SVC是LIBSVM库的包装，而LinearSVC是LIBLINEAR

的包装

LinearSVC的速度通常比SVC快{strong> ，并且可以处理更大的数据集，但只能使用线性核，因此得名。因此，区别不在于表述，而在于实现方法。

引用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.