SVM: 一种分类器，采用最大化分类间隔进行优化参数。

关于这个分类器两点比较重要：

1）SMO优化算法需要掌握， 可以具体参看两篇文章，John Platt的文章

以及“Improvements to Platt s SMO algorithm for SVM Classifier Design”

2）核函数的使用，如何将核函数使用到SVM中，核函数就是空间转换的函数，

说白了就是距离计算函数，如何将同类之间的距离计算的比较近，如何将低维空间转换到易于分类的高维空间。

我自己看书写的程序，采用Python实现，注释比较多-SVM: a classification, a classification intervals to maximize the use of optimization parameters.

About this classifier is more important points:

   　1) SMO optimization algorithms need to know, you can see the specific two articles, John Platt article

       　And "Improvements to Platt s SMO algorithm for SVM Classifier Design"

   　2) the use of nuclear function, how to use the SVM kernel function, the kernel function is a function space conversion,

That white is the distance calculation function, how relatively close distance between similar calculations, how to convert low-dimensional space to a high-dimensional space is easy classification.

I see myself writing program, using Python, notes more