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 and regularization term is crucial.

• SVMs do not directly provide probability estimates, these are calculated using an expensive five-fold cross-validation.