The RBF SVM method is a supervised learning algorithm designed to find an optimal separating hyperplane between classes. It aims to maximize the margin between classes to improve model reliability and reduce errors on new data.
The algorithm addresses non-linear data separation by mapping input data into a higher-dimensional space. In this space, classes that are mixed in lower dimensions can be separated by a hyperplane.
The model uses a weight vector to determine the orientation of the hyperplane and a bias term to control its position. The weight vector defines the importance of specific features, while the bias allows the boundary to shift away from the origin.
A key concept in SVM is the support vector, which refers to training samples located on the margin boundary that define the hyperplane's parameters. The algorithm seeks to maximize the gap between these support vectors.
The author notes that functional margins can be misleading because multiplying the hyperplane equation by a constant increases the margin value without changing the boundary, potentially causing the model to artificially inflate its confidence.
