Support Vector Machines

This is a machine learning model.

Summary

Support Vector Machines are supervised models that take positive and negative instances of classes in some feature space as training instances, and learn a linear decision boundary between the two classes. Unlike probability based methods such as logistic regression that attempt to learn a hyperplane that maximizes the likelihood of the training data, SVMs rely on margin and learn hyperplanes that maximize the distance separation between positive and negative instances. For cases where the training data isn't perfectly separable, they make use of slack variables.

Advantages

• They are more robust to overfitting, since the final model relies only on support vectors, as opposed to all the instances of the training set.
• The use of the Kernel trick allows us to efficiently use custom kernels for any given task
• They can easily handle very high dimensional data

Applications

They are applicable for a variety of binary as well as multiclass classification problems such as text classification, and have recently also been applied for regression problems.