Support Vector Machines

Support Vector Machines (SVM) are supervised learning models used for classification and regression tasks in machine learning. They work by finding the hyperplane that best separates data points of different classes in a high-dimensional space. One of the main characteristics of SVM is its ability to handle high-dimensional data effectively, making it suitable for applications such as image recognition and bioinformatics. Additionally, SVM can utilize different kernel functions to transform the input data, allowing for non-linear decision boundaries. Common use cases include text categorization, image classification, and hand-written digit recognition.