kNN and SVM represent different app

kNN and SVM represent different approaches to learning. Each approach implies different model for the underlying data.

SVM assumes there exist a hyper-plane seperating the data points (quite a restrictive assumption), while kNN attempts to approximate the underlying distribution of the data in a non-parametric fashion (crude approximation of parsen-window estimator).

You'll have to look at the specifics of your scenario to make a better decision as to what algorithm and configuration are best used.