Decision trees [32]
implement functions which are
piecewise constant on rectangular areas
parallel to the coordinate axes .
Such an approach can be written in tree structure
with nodes only performing comparisons
of the form or which allows a very effective
hardware implementation.
Such a piecewise constant approach can be written in the form
(407) |
An overview over different variants of decision trees together with a comparison with rule-based systems, neural networks (see Section 4.9) techniques from applied statistics like linear discriminants, projection pursuit (see Section 4.8) and local methods like for example -nearest neighbors methods (NN), Radial Basis Functions (RBF), or learning vector quantization (LVQ) is given in [158].