one class svm

One-class SVM (Support Vector Machine) is a type of machine learning algorithm used for anomaly detection. It is a type of unsupervised learning, meaning that it is trained on only one class of data (inliers) and then used to identify anomalies or outliers in new data.

The algorithm works by creating a hyperplane that separates the inliers from the outliers. This hyperplane is created in such a way that it maximizes the margin between the hyperplane and the inliers. The hyperplane is then used to predict whether new data points are inliers or outliers.

One-class SVM is commonly used for fraud detection, intrusion detection, and outlier detection in various fields such as finance, cybersecurity, and healthcare.