Abstrakt:
Learning methods with linear computational complexity
O(nd) in number of samples and their dimension often
give results that are better or at least not worse that more
sophisticated and slower algorithms. This is demonstrated for
many benchmark datasets downloaded from the UCI Machine
Learning Repository. Results provided in this paper should be
used as a reference for estimating usefulness of new learning
algorithms. Methods with higher than linear complexity should
provide significantly better results than those presented in this
paper to justify their use.
