ИСТИНА

We consider the problem of multi-modal regression estimation un- der the assumption that a kernel-based approach is applicable within each par- ticular modality. The Cartesian product of the linear spaces into which the re- spective kernels embed the output scales of single sensors is employed as an appropriate joint scale corresponding to the idea of combining modalities at the sensor level. This contrasts with the commonly adopted method of combining classifiers inferred from each specific modality. However, a significant risk in combining linear spaces is that of overfitting. To address this, we set out a sto- chastic method for encompassing modal-selectivity that is intrinsic to (that is to say, theoretically contiguous with) the selected kernel-based approach.