Support Vector Machine (SVM)
Support Vector Machine (SVM) is a supervised machine learning algorithm that finds an optimal boundary for separating data points into different classes, while maximizing the distance between the classes.
What is Support Vector Machine (SVM)?
An SVM identifies a decision boundary, called a hyperplane, that separates data points from different classes. It focuses on the closest data points to the boundary, known as support vectors, which determine the position of the optimal boundary. SVMs can also use kernel functions to handle non-linear relationships.
Why is Support Vector Machine (SVM) Important?
SVMs can perform well with high-dimensional data and can create effective decision boundaries even when datasets are relatively small. They are also flexible because different kernel functions can be used for complex classification problems.
Common use cases
Support Vector Machines are commonly used for text classification, image recognition, spam detection, handwriting recognition, and other classification and regression tasks.