Function approximation model ensembles and their application to the simultaneous determination of sample categories and positions

This paper uses multiple approximation model ensembles to solve a multi-input multi-output learning task. An ensemble is on behalf of a specified class, and composed of several multi-input single-output (MISO) approximation models. An MISO model may be either a multivariable cubic polynomial, or a multi-variable quartic polynomial, or a single-hidden-layer perceptron. The number of ensembles is equal to that of the existing classes, and all the members in an ensemble are trained only by the samples from the represented category. The ensemble in which all the members have the most identical viewpoint finally determines the label and position of one sample. The "most identical viewpoint" can be scaled by the corrected relative standard deviation. The proposed method is verified to be effective by a synthetic dataset.