1 research outputs found
On Deep Ensemble Learning from a Function Approximation Perspective
In this paper, we propose to provide a general ensemble learning framework
based on deep learning models. Given a group of unit models, the proposed deep
ensemble learning framework will effectively combine their learning results via
a multilayered ensemble model. In the case when the unit model mathematical
mappings are bounded, sigmoidal and discriminatory, we demonstrate that the
deep ensemble learning framework can achieve a universal approximation of any
functions from the input space to the output space. Meanwhile, to achieve such
a performance, the deep ensemble learning framework also impose a strict
constraint on the number of involved unit models. According to the theoretic
proof provided in this paper, given the input feature space of dimension d, the
required unit model number will be 2d, if the ensemble model involves one
single layer. Furthermore, as the ensemble component goes deeper, the number of
required unit model is proved to be lowered down exponentially