一些好的 SVM 教程的指针
我一直在努力掌握支持向量机的基础知识，并下载并阅读了许多在线文章。 但仍然无法掌握它。
我想知道，是否有一些
- 很好的教程
- 示例代码可以用于理解
或其他东西，您可以想到，这将使我能够轻松学习 SVM 基础知识。
PS：我不知何故设法学习了PCA（主成分分析）。 顺便说一句，你们可能会猜到我正在研究机器学习。
I have been trying to grasp the basics of Support Vector Machines, and downloaded and read many online articles. But still am not able to grasp it.
I would like to know, if there are some
- nice tutorial
- sample code which can be used for understanding
or something, that you can think of, and that will enable me to learn SVM Basics easily.
PS: I somehow managed to learn PCA (Principal Component Analysis).
BTW, you guys would have guessed that I am working on Machine Learning.
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我会获取 R 的副本，安装 e1071 包很好地包装了 libsvm，并尝试在您最喜欢的数据集上获得良好的结果。
如果您刚刚了解 PCA，那么查看具有比案例更多的预测变量的数据（例如，微阵列基因表达谱、时间序列、分析化学光谱等）并比较 PCA 预测变量的线性回归可能会提供丰富的信息在原始预测变量上使用 SVM。
其他答案中有很多很棒的参考资料，但我认为在阅读里面的内容之前先尝试一下黑匣子是有价值的。
I would grab a copy of R, install the e1071 package which nicely wraps libsvm, and try to get good results on your favorite data sets.
If you just figured out PCA, it might be informative to look at data with many more predictors than cases (e.g., microarray gene expression profiles, time series, spectra from analytical chemistry, etc.) and compare linear regression on the PCA'd predictors with SVM on the raw predictors.
There are a lot of great references in the other answers, but I think there's value in playing around with the black box before you read what's inside.
假设您了解基础知识（例如最大边距分类器、构建内核），请解决问题集 2 (斯坦福大学机器学习课程的讲义#5）。 有答案键和 整个过程他都握着你的手。 使用讲义3 & 视频 #7-8 作为参考。
如果您不了解基础知识，请观看之前的视频。
Assuming you know the basics (eg max margin classifiers, constructing a kernel), solve Problem Set 2 (handout #5) of that stanford machine learning course. There's answer keys & he holds your hand through the whole process. Use Lecture notes 3 & video #7-8 as references.
If you don't know the basics, watch earlier videos.
SVM 分类实用指南libsvm
PyML 教程 PyML
我认为1实用，3 很容易理解。
A practical guide to SVM classification for libsvm
PyML Tutorial for PyML
I think 1 is practical for use, 3 is clear for understanding.
很多关于 SVM 的视频讲座：
http://videolectures.net/Top/Computer_Science/Machine_Learning/Kernel_Methods/Support_Vector_Machines/
我发现 Colin Campbell 的文章非常有用。
Lots of video lectures on SVM:
http://videolectures.net/Top/Computer_Science/Machine_Learning/Kernel_Methods/Support_Vector_Machines/
I found the one by Colin Campbell to be very useful.
这是关于 SVM 的非常好的初学者教程：
SVM 解释
我一直认为 StompChicken 推荐的教程有点令人困惑，因为他们直接讨论边界和 VC 统计并试图找到最佳机器等。 不过，如果您已经了解基础知识，那就太好了。
This is a very good beginner's tutorial on SVM:
SVM explained
I always thought StompChicken's recommended tutorial was a bit confusing in the way that they jump right into talking about bounds and VC statistics and trying to find the optimal machine and such. It's good if you already understand the basics, though.
SVM 教程的标准建议是用于模式识别的支持向量机教程 克里斯托弗·伯吉斯。 另一个了解 SVM 的好地方是机器学习斯坦福大学课程（第 6-8 讲介绍了支持向量机）。 这两者都非常理论化，并且涉及大量数学知识。
至于源代码； SVMLight, libsvm 和 TinySVM 均已开放-source，但是代码不太容易理解。 我没有仔细研究它们中的每一个，但 TinySVM 的源代码可能是最容易理解的。 本文中还有 SMO 算法的伪代码实现。
The standard recommendation for a tutorial in SVMs is A Tutorial on Support Vector Machines for Pattern Recognition by Christopher Burges. Another good place to learn about SVMs is the Machine Learning Course at Stanford (SVMs are covered in lectures 6-8). Both these are quite theoretical and heavy on the maths.
As for source code; SVMLight, libsvm and TinySVM are all open-source, but the code is not very easy to follow. I haven't looked at each of them very closely, but the source for TinySVM is probably the is easiest to understand. There is also a pseudo-code implementation of the SMO algorithm in this paper.