  In an educational setting, a teacher plays a crucial role in various
classroom teaching patterns. Similarly, mirroring this aspect of human
learning, the learning using privileged information (LUPI) paradigm introduces
additional information to instruct learning models during the training stage. A
different approach to train the twin variant of the regression model is
provided by the new least square twin support vector regression using
privileged information (LSTSVR-PI), which integrates the LUPI paradigm to
utilize additional sources of information into the least square twin support
vector regression. The proposed LSTSVR-PI solves system of linear equations
which adds up to the efficiency of the model. Further, we also establish a
generalization error bound based on the Rademacher complexity of the proposed
model and incorporate the structural risk minimization principle. The proposed
LSTSVR-PI fills the gap between the contemporary paradigm of LUPI and classical
LSTSVR. Further, to assess the performance of the proposed model, we conduct
numerical experiments along with the baseline models across various
artificially generated and real-world datasets. The various experiments and
statistical analysis infer the superiority of the proposed model. Moreover, as
an application, we conduct experiments on time series datasets, which results
in the superiority of the proposed LSTSVR-PI.
