A Practical Guide to Support Vector Classification

SVM (Support Vector Machine) is a useful technique for data classification. Even though people consider that it is easier to use than Neural Networks, however, users who are not familiar with SVM often get unsatisfactory results at first. Here we propose a “cookbook” approach which usually gives reasonable results. Note that this guide is not for SVM researchers nor do we guarantee the best accuracy. We also do not intend to solve challenging or difficult problems. Our purpose is to give SVM novices a recipe to obtain acceptable results fast and easily. [via]

http://ntu.csie.org/~cjlin/papers/guide/guide...