PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Weighted Sampling for Large-Scale Boosting
Zdenek Kalal, Jiri Matas and Krystian Mikolajczyk
BMVC 2008.


This paper addresses the problem of learning from very large databases where batch learning is impractical or even infeasible. Bootstrap is a popular technique applicable in such situations. We show that sampling strategy used for bootstrapping has a significant impact on the resulting classifier performance. We design a new general sampling strategy "quasi-random weighted sampling + trimming" (QWS+) that includes well established strategies as special cases. The QWS+ approach minimizes the variance of hypothesis error estimate and leads to significant improvement in performance compared to standard sampling techniques. The superior performance is demonstrated on several problems including profile and frontal face detection.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Machine Vision
ID Code:6949
Deposited By:Zdenek Kálal
Deposited On:17 June 2010