Desktop Survival Guide
by Graham Williams
A Support Vector Machine (SMV) searches for so called support vectors which are data points that are found to lie at the edge of an area in space which is a boundary from one class of points to another. In the terminology of SVM we talk about the space between regions containing data points in different classes as being the margin between those classes. The support vectors are used to identify a hyperplane (when we are talking about many dimensions in the data, or a line if we were talking about only two dimensional data) that separates the classes. Essentially, the maximum margin between the separable classes is found. An advantage of the method is that the modelling only deals with these support vectors, rather than the whole training dataset, and so the size of the training set is not usually an issue. If the data is not linearly separable, then kernels are used to map the data into higher dimensions so that the classes are linearly separable. Also, Support Vector Machines have been found to perform well on problems that are non-linear, sparse, and high dimensional. A disadvantage is that the algorithm is sensitive to the choice of parameter settings, making it harder to use, and time consuming to identify the best.