NASA Logo

NTRS

NTRS - NASA Technical Reports Server

Collections About News Help Login
Back to Results
Learning classification treesAlgorithms for learning classification trees have had successes in artificial intelligence and statistics over many years. How a tree learning algorithm can be derived from Bayesian decision theory is outlined. This introduces Bayesian techniques for splitting, smoothing, and tree averaging. The splitting rule turns out to be similar to Quinlan's information gain splitting rule, while smoothing and averaging replace pruning. Comparative experiments with reimplementations of a minimum encoding approach, Quinlan's C4 and Breiman et al. Cart show the full Bayesian algorithm is consistently as good, or more accurate than these other approaches though at a computational price.
Document ID
19920016875
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Buntine, Wray (Research Inst. for Advanced Computer Science Moffett Field, CA., United States)
Date Acquired
September 6, 2013
Publication Date
February 19, 1991
Subject Category
Cybernetics
Report/Patent Number
NAS 1.15:107883
NASA-TM-107883
FIA-90-12-19-01
Meeting Information
Meeting: International Workshop on Artificial Intelligence and Statistics
Location: Fort Lauderdale, FL
Country: United States
Start Date: January 3, 1991
End Date: January 5, 1991
Accession Number
92N26118
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.

Related Records

There are no records associated with this record.
No Preview Available