A bit of text we are proud to steal from our good friend Joseph Rickert:

Then, for some very readable background material on SVMs I recommend section 13.4 of Applied Predictive Modeling and sections 9.3 and 9.4 of Practical Data Science with R by Nina Zumel and John Mount. You will be hard pressed to find an introduction to kernel methods and SVMs that is as clear and useful as this last reference.

For more on SVMs see the original article on the Revolution Analytics blog.

Categories: Administrativia

Tagged as: Kernel Methods Practical Data Science Practical Data Science with R Support Vector Machines SVM

jmount

Data Scientist and trainer at Win Vector LLC. One of the authors of Practical Data Science with R.