# SVM In machine learning, **support vector machines**( **SVMs**, also **support vector networks**) are supervised learning models wit. Given a set of training examples, each marked as belonging to one or the other of two categories, an SVM training algorithm builds a model that assigns new examples to one category or the other, making it a non-probabilistic binary linear classifier(although methods such as Platt scaling exist to use SVM in a probabilistic classification setting). The advantages of support vector machines are: * Effective in high dimensional spaces. * Still effective in cases where number of dimensions is greater than the number of samples. * Uses a subset of training points in the decision function (called support vectors), so it is also memory efficient. * Versatile: different Kernel functions can be specified for the decision function. Common kernels are provided, but it is also possible to specify custom kernels. The disadvantages of support vector machines include: * If the number of features is much greater than the number of samples, avoid over-fitting in choosing [Kernel functions](http://scikit-learn.org/stable/modules/svm.html#svm-kernels) and regularization term is crucial. * SVMs do not directly provide probability estimates, these are calculated using an expensive five-fold cross-validation. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://alfredo-reyes-montero.gitbook.io/soft-computing/applications/matlab/svm.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.