Least squares support vector machines

Least Squares Support Vector Machines (LS-SVM) are a type of machine learning model used to classify data or predict outcomes. They work by finding the best boundary (or hyperplane) that separates different groups in the data, aiming to maximize the margin between classes while minimizing errors. Unlike traditional SVMs, LS-SVM simplifies calculations by using a least squares approach, making the training process faster and easier to solve mathematically. This results in a model that is both efficient and effective at handling complex data patterns, suitable for tasks like image recognition, diagnosis, or forecasting.