Training one-class support vector machines (one-class SVMs) involves solving a quadratic programming (QP) problem. By increasing the number of training samples, solving this QP problem becomes intractable. In this paper, we describe a modified Pegasos algorithm for fast training of one-class SVMs. We show that this algorithm is much faster than the standard one-class SVM without loss of performance in the case of linear kernel.