实现支持向量回归的最佳 MATLAB 工具箱？
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Best MATLAB toolbox that implements Support Vector Regression?
提问by Degvik
In this Wikipedia article about SVMthere are a number of links to different implementations of MATLAB toolboxes for Support Vector Machines. Could anyone suggest which of these is best in terms of speed, ease of use, etc.?
在这篇关于 SVM 的维基百科文章中，有许多链接指向支持向量机的 MATLAB 工具箱的不同实现。任何人都可以建议在速度、易用性等方面哪个最好？
采纳答案by Jason Sundram
I've used libSVM. It is pretty fast and easy, and provides some useful tools, too. There are some examples of it in use here. The other nice thing is that there are implementations in C++ and Java, too, so if you find yourself needing to develop outside of Matlab (to turn a prototype into something speedy, for example), you will have a familiar interface to work with.
我用过libSVM。它非常快速和简单，并且还提供了一些有用的工具。目前，使用它的一些例子在这里。另一个好处是 C++ 和 Java 也有实现，所以如果您发现自己需要在 Matlab 之外进行开发（例如，将原型转换为快速的东西），您将拥有一个熟悉的界面来使用。
回答by KennyMorton
回答by Amro
You can always use MathWorks implementation of SVM in the Bioinformatics Toolbox with the functions: svmtrainand svmclassify, which as usual have excellent documentation
您始终可以在 Bioinformatics Toolbox 中使用 MathWorks 实现的 SVM 和函数：svmtrain和svmclassify，它们像往常一样具有出色的文档
回答by devin
This doesn't answer your question directly, but if you want to speed up an M script look into the embedded matlab toolbox and MEX functions. Basically, you can use these tools to compile your M scripts, I've done it and I get an order of magnitude performance gain minimum. The people at the MW say you can get 100 fold improvement.
这不会直接回答您的问题，但如果您想加快 M 脚本的速度，请查看嵌入式 matlab 工具箱和 MEX 函数。基本上，您可以使用这些工具来编译您的 M 脚本，我已经完成了，并且我获得了一个数量级的最低性能增益。MW 的人说你可以获得 100 倍的改进。
