Online Bagging and Boosting
NASA Technical Reports Server (NASA)Published 4 January 2001Open access
Nikunj C. Oza, Stuart Russell
Citations333
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
Abstract
Bagging and boosting are well-known ensemble learning methods. They combine multiple learned base models with the aim of improving generalization performance. To date, they have been used primarily in batch mode, and no effective online versions have been proposed. We present simple online bagging and boosting algorithms that we claim perform as well as their batch counterparts.
Keywords
Social SciencesBusiness, Management and Accounting