SMO (Sequential Minimal Optimization)

Sequential Minimal Optimization (SMO) is an algorithm used to train Support Vector Machines (SVMs), a type of machine learning model for classification tasks. It simplifies the problem of finding the best boundary between different data groups by breaking it into small, manageable steps. Specifically, SMO adjusts only two data points’ parameters at a time, making complex calculations more efficient. This process repeats until the model perfectly or satisfactorily separates the data, enabling the SVM to predict new data accurately. SMO's efficiency makes training large datasets faster and more practical.