> For the complete documentation index, see [llms.txt](https://alfredo-reyes-montero.gitbook.io/soft-computing/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://alfredo-reyes-montero.gitbook.io/soft-computing/applications/matlab/svm.md).

# 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.