NASA Logo

NTRS

NTRS - NASA Technical Reports Server

Collections About News Help Login
Back to Results
Online Bagging and BoostingBagging and boosting are two of the most well-known ensemble learning methods due to their theoretical performance guarantees and strong experimental results. However, these algorithms have been used mainly in batch mode, i.e., they require the entire training set to be available at once and, in some cases, require random access to the data. In this paper, we present online versions of bagging and boosting that require only one pass through the training data. We build on previously presented work by presenting some theoretical results. We also compare the online and batch algorithms experimentally in terms of accuracy and running time.
Document ID
20050239012
Acquisition Source
Ames Research Center
Document Type
Preprint (Draft being sent to journal)
Authors
Oza, Nikunji C. (NASA Ames Research Center Moffett Field, CA, United States)
Date Acquired
September 7, 2013
Publication Date
January 1, 2005
Subject Category
Mathematical And Computer Sciences (General)
Meeting Information
Meeting: IEEE Conference on Systems, Man, and Cybernetics, Special Session on Ensemble Methods for Extreme Environments
Location: Waikoloa, HI
Country: United States
Start Date: October 10, 2005
End Date: October 12, 2005
Sponsors: Institute of Electrical and Electronics Engineers
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