This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
A Recursive Partitioning Decision Rule for Nonparametric Classification
April 1977 (vol. 26 no. 4)
pp. 404-408
J.H. Friedman, Stanford Linear Accelerator Center
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.
Index Terms:
Adaptively generated features, Kolmogorov-Smirnoff distance, nonparametric classification, recursive partitioning.
Citation:
J.H. Friedman, "A Recursive Partitioning Decision Rule for Nonparametric Classification," IEEE Transactions on Computers, vol. 26, no. 4, pp. 404-408, April 1977, doi:10.1109/TC.1977.1674849
Usage of this product signifies your acceptance of the Terms of Use.