On data classification by iterative linear partitioning
We analyze theoretically the generalization properties of multi-class data classification techniques that are based on iteratively partitioning the data points by hyperplanes. A special case is that in which the data points of different classes are separated by a number of parallel hyperplanes, and we investigate the algorithmics of finding a suitable partitioning in this case.
hyperplanes pattern classification multi-class classification decision lists