  Zero-shot learning (ZSL) can be defined by correctly solving a task where no
training data is available, based on previous acquired knowledge from
different, but related tasks. So far, this area has mostly drawn the attention
from computer vision community where a new unseen image needs to be correctly
classified, assuming the target class was not used in the training procedure.
Apart from image classification, only a couple of generic methods were proposed
that are applicable to both classification and regression. These learn the
relation among model coefficients so new ones can be predicted according to
provided conditions. So far, up to our knowledge, no methods exist that are
applicable only to regression, and take advantage from such setting. Therefore,
the present work proposes a novel algorithm for regression problems that uses
data drawn from trained models, instead of model coefficients. In this case, a
shape analyses on the data is performed to create a statistical shape model and
generate new shapes to train new models. The proposed algorithm is tested in a
theoretical setting using the beta distribution where main problem to solve is
to estimate a function that predicts curves, based on already learned
different, but related ones.
