Ensembles of hybrid intelligent experts: extending the power of optimal linear combiners

In the present paper we generalize the idea of optimal linear combiners that are used to aggregate information from different sources providing estimates about a specific quantity. Two linear models are introduced, along with their analysis, which combine related components of information when more than one variable is to be predicted. The models´ purpose is to produce point estimates of better accuracy in terms of mean squared error. Experimental results dealing with a functional approximation problem demonstrate that the generalized optimal linear combiners suggested yield higher accuracy when compared to other combiners such as the simple average, or the conventional optimal linear combiners