如何解决scikit中的SVC与LinearSVC学习：损失函数的差异
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。
- 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
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.
版权声明：本文内容由互联网用户自发贡献，该文观点与技术仅代表作者本人。本站仅提供信息存储空间服务，不拥有所有权，不承担相关法律责任。如发现本站有涉嫌侵权/违法违规的内容， 请发送邮件至 dio@foxmail.com 举报，一经查实，本站将立刻删除。
