title: Custom kernels for SVM, when to apply them?
body: <p>I am new to machine learning field and right now trying to get a grasp of how the most common learning algorithms work and understand when to apply each one of them. At the moment I am learning on how Support Vector Machines work and have a question on custom kernel functions.<br>
  There is plenty of information on the web on more standard (linear, RBF, polynomial) kernels for SVMs. I, however, would like to understand when it is reasonable to go for a custom kernel function. My questions are: </p>
  
  <p>1) What are other possible kernels for SVMs?<br>
  2) In which situation one would apply custom kernels?<br>
  3) Can custom kernel substantially improve prediction quality of SVM?</p>