I found some useful recommendations for using non-linear discrimination techniques in the book "Statistical Pattern Recognition" by Andrew Webb. In this page, I will summarize the main points of the recommendations which I think are very interesting:
Non-liner discriminations methods for classification now are very popular and easy to implement. There are also many available sources of software both free or commerical for doing them. However, before using non-linear disrimination methods, we should consider if they are really neccessary and the applying them can really improve the performance. The first thing we need consider is the dicision boundary is realy non-linear. Someone after doing some linear discrimination and getting bad classification results, will move immediately to Neural network or Support Vector Machine with nonlinear kernel with the hope that the result will be significantly improved. However this is not always the case. If two classes are not seperable, whatever, even very complicated model we use won't gu
