In affective computing, two main paradigms are used to represent emotion: categorical representation and dimensional description in continuous space. Therefore, the emotion recognition task can be treated as a classification or regression. The main aim of this study is to investigate the relation between these two representations and propose a classification pipeline that uses only dimensional annotation. Our approach contains a neural regressor which predicts a vector of arousal, valence and dominance values for a given speech segment. This vector can be interpreted as an emotional category using a mapping algorithm. We investigate the performances of a neural network architectures, and three mapping algorithms on two corpora. Our study shows the limitation and an advantage of the emotion classification via regression approach.