A Support Vector Machine (SVM) is a powerful supervised machine learning model used for classification and regression tasks. The objective of the SVM algorithm is to find a hyperplane in an N-dimensional space (where N is the number of features) that distinctly classifies the data points. The 'support vectors' are the data points that are closest to the hyperplane and influence its position and orientation. SVMs are particularly effective in high-dimensional spaces.
The original SVM algorithm was invented by Vladimir Vapnik and Alexey Chervonenkis in 1963. The current standard incarnation (soft margin) was proposed by Corinna Cortes and Vapnik in 1995. It was one of the most popular algorithms before the deep learning era.
SVMs are widely used in text categorization, image classification, bioinformatics (protein classification), and handwriting recognition. They are prized for their theoretical guarantees and ability to handle non-linear data using the 'kernel trick'.