Title :
A Recursive Partitioning Decision Rule for Nonparametric Classification
Author :
Friedman, Jerome H.
Author_Institution :
Stanford Linear Accelerator Center
fDate :
4/1/1977 12:00:00 AM
Abstract :
A new criterion for deriving a recursive partitioning decision rule for nonparametric classification is presented. The criterion is both conceptually and computationally simple, and can be shown to have strong statistical merit. The resulting decision rule is asymptotically Bayes´ risk efficient. The notion of adaptively generated features is introduced and methods are presented for dealing with missing features in both training and test vectors.
Keywords :
Adaptively generated features, Kolmogorov-Smirnoff distance, nonparametric classification, recursive partitioning.; Covariance matrix; Distribution functions; IEL; Linear accelerators; Manufacturing; Partitioning algorithms; Scattering; Adaptively generated features, Kolmogorov-Smirnoff distance, nonparametric classification, recursive partitioning.;
Journal_Title :
Computers, IEEE Transactions on
DOI :
10.1109/TC.1977.1674849