What is SVMs?

Support Vector Machines (SVMs) are a powerful and versatile class of supervised machine learning algorithms used for classification, regression, and outlier detection. They work by finding the optimal hyperplane that maximizes the margin between different classes in the feature space. SVMs are effective in high dimensional spaces and can handle non-linear data through the use of kernel functions, such as polynomial or radial basis functions, which implicitly map the input data into higher-dimensional spaces where linear separation becomes possible. Common applications include image classification, text categorization, bioinformatics, and medical diagnosis.