Support Vector Machines (SVM) are a set of supervised learning methods used for classification, regression, and outliers detection. They are well-known for their ability to handle high-dimensional data and find the optimal hyperplane that separates different classes in the dataset with maximum margin. In the context of nanotechnology, SVMs can be used to analyze and predict complex patterns in nanoscale materials and processes.
