Deep learning for multi-output regression using gradient boosting

SEYEDSAMAN EMAMI, Gonzalo Martínez Muñoz

Abstract: This paper proposes a gradient boosting framework for multi-output regression using deep neural networks. The model is trained sequentially by adding dense layers that learn residuals while keeping previous layers frozen. Results show improved performance and reduced training complexity.