  Multi-task learning (MTL) has emerged as a promising topic of machine
learning in recent years, aiming to enhance the performance of numerous related
learning tasks by exploiting beneficial information. During the training phase,
most of the existing multi-task learning models concentrate entirely on the
target task data and ignore the non-target task data contained in the target
tasks. To address this issue, Universum data, that do not correspond to any
class of a classification problem, may be used as prior knowledge in the
training model. This study looks at the challenge of multi-task learning using
Universum data to employ non-target task data, which leads to better
performance. It proposes a multi-task twin support vector machine with
Universum data (UMTSVM) and provides two approaches to its solution. The first
approach takes into account the dual formulation of UMTSVM and tries to solve a
quadratic programming problem. The second approach formulates a least-squares
version of UMTSVM and refers to it as LS-UMTSVM to further increase the
generalization performance. The solution of the two primal problems in
LS-UMTSVM is simplified to solving just two systems of linear equations,
resulting in an incredibly simple and quick approach. Numerical experiments on
several popular multi-task data sets and medical data sets demonstrate the
efficiency of the proposed methods.
