Deep Learning
Deep Learning Concepts
Support Vector Machines
Support Vector Machines A Support Vector Machine (SVM) is a powerful supervised machine learning model used for classification. An SVM makes classifications by defining a decision boundary and then seeing what side of the boundary an unclassified point falls on. Decision boundaries get defined, by using a training set of classified points. Decision boundaries exist even when your data has more than two features. If there are three features, the decision boundary is now a plane rather than a line. As the number of dimensions grows past 3, it becomes very difficult to visualize these points in space. Nonetheless, SVMs can still find a decision boundary. However, rather than being a separating line, or a separating plane, the decision boundary is called a separating hyperplane . Optimal Decision Boundaries In general, we want our decision boundary to be as far away from training points as possible. Maximizing the dis...
Comments
Post a Comment