%T An Empirical Study of Training and Testing Error in Boosting
%A David D. Latham
%R Technical Report TR2001-394
%I Dartmouth College, Computer Science
%C Hanover, NH
%D June 2001
%U http://www.cs.dartmouth.edu/reports/TR2001-394.ps.Z
%X
 Bounds have been proven for both training and testing error for the boosting
 algorithm AdaBoost, but in practice neither seem to produce a particularly
 tight bound.  In this paper we share some observations of these bounds from
 empirical results, and then explore some properties of the algorithm with an
 eye towards finding an improved bound for the performance of AdaBoost.
 Based on our empirical evidence, the error of a hypothesis which labels
 examples probabilistically based upon the confidence of the vote of the weak
 hypotheses forms a tighter bound for the training error.
%Z
 Senior Honors Thesis.  Advisor: Jay Aslam.