Introduction - If you have any usage issues, please Google them yourself
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