Ching-Hao Yu

  • Citations Per Year
Learn More
Bagging and boosting are two general techniques for building predictors based on small samples from a dataset. We show that boosting can be parallelized, and then present performance results for parallelized bagging and boosting using OC1 decision trees and two standard datasets. The main results are that sample sizes limit achievable accuracy, regardless(More)
  • 1