  In school, a teacher plays an important role in various classroom teaching
patterns. Likewise to this human learning activity, the learning using
privileged information (LUPI) paradigm provides additional information
generated by the teacher to 'teach' learning models during the training stage.
Therefore, this novel learning paradigm is a typical Teacher-Student
Interaction mechanism. This paper is the first to present a random vector
functional link network based on the LUPI paradigm, called RVFL+. Rather than
simply combining two existing approaches, the newly-derived RVFL+ fills the gap
between classical randomized neural networks and the newfashioned LUPI
paradigm, which offers an alternative way to train RVFL networks. Moreover, the
proposed RVFL+ can perform in conjunction with the kernel trick for highly
complicated nonlinear feature learning, which is termed KRVFL+. Furthermore,
the statistical property of the proposed RVFL+ is investigated, and we present
a sharp and high-quality generalization error bound based on the Rademacher
complexity. Competitive experimental results on 14 real-world datasets
illustrate the great effectiveness and efficiency of the novel RVFL+ and
KRVFL+, which can achieve better generalization performance than
state-of-the-art methods.
