Maximum Margin Classifier

A Maximum Margin Classifier is a machine learning method that finds the best way to separate different groups of data by drawing a boundary, called a hyperplane. It aims to maximize the distance (margin) between this boundary and the nearest data points from each group. By doing so, it improves the classifier's ability to correctly categorize new data points and reduces errors. Essentially, it focuses on creating a clear, wide gap between different classes, leading to more reliable and robust predictions. This approach underpins support vector machines (SVM), a powerful classification technique.