%0 Journal Article
	%A Doaa Hegazy and  Joachim Denzler
	%D 2008
	%J International Journal of Computer and Information Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 21, 2008
	%T Performance Comparison and Evaluation of AdaBoost and SoftBoost Algorithms on Generic Object Recognition
	%U https://publications.waset.org/pdf/5048
	%V 21
	%X SoftBoost is a recently presented boosting algorithm,
which trades off the size of achieved classification margin and
generalization performance. This paper presents a performance
evaluation of SoftBoost algorithm on the generic object recognition
problem. An appearance-based generic object recognition
model is used. The evaluation experiments are performed using
a difficult object recognition benchmark. An assessment with respect
to different degrees of label noise as well as a comparison to
the well known AdaBoost algorithm is performed. The obtained
results reveal that SoftBoost is encouraged to be used in cases
when the training data is known to have a high degree of noise.
Otherwise, using Adaboost can achieve better performance.
	%P 2893 - 2897