Final answer:
The distance between a support plane and the decision boundary in Support Vector Machines (SVMs) is known as the margin, which SVMs aim to maximize to ensure robust class separation.
Step-by-step explanation:
The distance between a support plane and the decision boundary in the context of machine learning, specifically in Support Vector Machines (SVMs), is known as the margin. The support plane is a hyperplane that is parallel to the decision boundary and touches the data points that are closest to this boundary, and these points are known as support vectors. In an SVM, one of the primary objectives is to maximize the margin in order to improve the model's generalization capabilities on new, unseen data. A larger margin is aimed at providing a clear, robust separation between the classes that the SVM is trying to classify.