one class SVM

One-class SVM (Support Vector Machine) is a type of SVM algorithm where the training data contains only one class. The objective of the one-class SVM is to learn the boundaries of the data from that one class so that it can classify new data points as either belonging to the same class or not.

This algorithm is used for anomaly detection or novelty detection, where the goal is to identify whether a new observation is an outlier or not. In one-class SVM, the algorithm constructs a hyperplane that separates the data from the origin, and the hyperplane is optimized to minimize the distance between the origin and the hyperplane while maximizing the margin.

The one-class SVM is widely used in areas such as fraud detection, intrusion detection, and medical diagnosis. However, it has some limitations, such as being sensitive to the choice of parameters and the need for a large amount of training data to learn the boundaries of the data.