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

Hyper-Process Model: A Zero-Shot Learning algorithm for Regression Problems based on Shape Analysis