Multioutput Regression Neural Network Training via Gradient Boosting
Seyedsaman Emami, Gonzalo Martínez-Muñoz · 2022
A novel sequential procedure to train the final layers of a multi-output regression neural network (NN) based on Gradient Boosting is proposed, where the NN is an additive expansion of the Gradient Boosting.The method works by training portions of the network in an iterative manner in such a way that each new portion of the NN is learnt to compensate for the errors of the already trained portions, and the final result of the network forms by provided weight and the last hidden layer output.This is in contrast to the standard training of NNs in which the whole network is trained to learn the concept at hand.Extensive experiments show the good performance of the proposed method with respect to NN.