In conventional supervised learning, one searches for "vertical" patterns, connecting inputs directly to outputs. One can instead search for "horizontal" patterns, which go across the learning set, connecting one part of it with another. One way to do this is to pre-process the learning problem before feeding it to a conventional generalizer (like backpropagation). This pre-processing technique...

This paper introduces stacked generalization, a scheme for minimizing the generalization error rate of one or more generalizers. Stacked generalization works by deducing the biases of the generalizer(s) with respect to a provided learning set. This deduction proceeds by generalizing in a second space whose inputs are (for example) the guesses of the original generalizers when taught with part o...