 Sequential minimal optimization

Sequential Minimal Optimization (SMO) is an algorithm used for training support vector machines (SVM), which are a type of supervised machine learning model. Developed by John Platt in 1998, SMO provides a way to efficiently solve the optimization problem associated with training a SVM, specifically the quadratic programming problem that arises from maximizing the margin between different classes in the data.