Online boosting algorithm for regression with additive and multiplicative updates

In this paper, we propose a boosted regression algorithm in an online framework. We have a linear combination of the estimated output for each weak learner and weigh each of the estimated output differently by introducing ensemble coefficients. We then update the ensemble weight coefficients using both additive and multiplicative updates along with the stochastic gradient updates of the regression weight coefficients. We make the proposed...