Transfer Learning (TL) aims to transfer knowledge acquired in one problem,
the source problem, onto another problem, the target problem, dispensing with
the bottom-up construction of the target model. Due to its relevance, TL has
gained significant interest in the Machine Learning community since it paves
the way to devise intelligent learning models that can easily be tailored to
many different applications. As it is natural in a fast evolving area, a wide
variety of TL methods, settings and nomenclature have been proposed so far.
However, a wide range of works have been reporting different names for the same
concepts. This concept and terminology mixture contribute however to obscure
the TL field, hindering its proper consideration. In this paper we present a
review of the literature on the majority of classification TL methods, and also
a distribution-based categorization of TL with a common nomenclature suitable
to classification problems. Under this perspective three main TL categories are
presented, discussed and illustrated with examples
