Carr, Brittany M., authorAdams, Henry, advisorShipman, Patrick, committee memberFremstad, Anders, committee member2019-09-102019-09-102019https://hdl.handle.net/10217/197262A support vector machine, (SVM), is an algorithm which finds a hyperplane that optimally separates labeled data points in Rn into positive and negative classes. The data points on the margin of this separating hyperplane are called \emph{support vectors}. We study the possible configurations of support vectors for points in general position. In particular, we connect the possible configurations to Radon's theorem, which provides guarantees for when a set of points can be divided into two classes (positive and negative) whose convex hulls intersect. If the positive and negative support vectors in a generic SVM configuration are projected to the separating hyperplane, then these projected points will form a Radon configuration.born digitalmasters thesesengCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.support vector machinesRadon's theoremText