PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

On data classification by iterative linear partitioning
Martin Anthony
Discrete Applied Mathematics 2004. ISSN 0166-218X


We analyse 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.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Theory & Algorithms
ID Code:111
Deposited By:Martin Anthony
Deposited On:22 May 2004