LIBIRWLS: A parallel IRWLS library for full and budgeted SVMs

摘要

We present LIBIRWLS, a library that incorporates a parallel implementation of the Iterative Re-Weighted Least Squares (IRWLS) procedure, an alternative to quadratic programming (QP), for training of Support Vector Machines (SVMs). Although there are several methods for SVM training, the number of parallel libraries is very reduced. In particular, this library contains solutions to solve either full or budgeted SVMs making use of shared memory parallelization techniques.