Authorship verification is the task of, given a document and a candi- date author, determining whether or not the document was written by the candi- date author. Traditional approaches to authorship verification have revolved around a “candidate author vs. everything else” approach. Thus, perhaps the most important aspect of performing authorship verification on a document is the development of an appropriate distractor set to represent “everything not the candidate author”. The validity of the results of such experiments hinges on the ability to develop an appropriately representative set of distractor documents. Here, we propose a method for performing authorship verification without the use of a distractor set. Using only training data from the candidate author, we are able to perform authorship verification with high confidence (greater than 90% accuracy rates across a large corpus).