I have a question about the support vector machine (SVM) kernel trick. How do you find the boundaries of the training data set in kernel projected space? Is that the same boundaries as you can obtain in original input space? Does kernel mapping change the data distribution? I am new to SVM, can someone please give me a straightforward explanation or links to related information?