Heterogeneous adaptive systems

Most adaptive systems are homogenous, i.e. they are built from processing elements of the same type. MLP neu-ral networks and decision trees use nodes that partition the input space by hyperplanes. Other types of neural networks use nodes that provide spherical or ellipsoidal decision borders. This may not be the best inductive bias for a given data, frequently requiring large number of processing elements even in cases when simple solutions exist. In heterogeneous adaptive systems (HAS) different types of decision borders are used at each stage, enabling discovery of a most appropriate bias for the data. Neural, decision tree and similarity-based systems of this sort are described here. Results from a novel heterogeneous decision tree algorithm are presented as an example of this approach.