Support Vector Machines

Support Vector Machines (SVM) are a type of supervised learning algorithms used in machine learning and data mining. They are used to find an optimal separation line between points of different classes in a given dataset. The support vectors are the data points that are closest to the separation line and are used to define the margin and accuracy of the model. SVM is used in many areas such as text categorization, image processing, face detection, and bioinformatics. It is also used in financial forecasting and stock market analysis. SVM is an effective method for solving complex problems with high accuracy. It is a powerful algorithm that can be used for classification, regression, anomaly detection and clustering.