# SVM Train

SVM train stands for a process in machine learning that creates a Support Vector Machine (SVM) model by learning from big data. There are several ways how such a model can be trained and we provide an overview below. This overview includes the training parameters in the de-facto standard SVM implementation called libsvm that offers a tool called svm-train. More detailed pieces of information can be obtained here. **This overview explains a bit more in detail the different parameter options for the SVM training process.**.

**C-Support Vector Classification (C-SVC)**

This is a classification model based on SVMs including multi-class classification. **The cost (C) in this case refers to a soft-margin specifying how much error is allowed and thus represents a regularization parameter that prevents overfitting.** The range of the parameter C is from zero to infinity and the svm-train parameter is ‘-s svmtype’ whereby the svmtype in this case is 0 for C-SVC. Although some application domain have established some rules of thumb for the value of C it can be a bit hard to estimate it and use in practical situations. Often cross-validation is used to systematically determine the value. It is a regularization parameter that prevents overfitting. The range of the parameter C is from zero to infinity and the svm-train parameter is ‘-s svmtype’ whereby the svmtype in this case is 0 for C-SVC.

**nu-Support Vector Classification (nu-SVC)**

This is also a classification model based on SVMs including multi-class classification. **The nu in this case refers to values between 0 and 1 and thus represents a lower and upper bound on the number of examples that are support vectors and that lie on the wrong side of the hyperplane.**

## Details on SVM train

The following video is interesting in context: