DocumentCode
2507868
Title
Package Boosting for Readaption of Cascaded Classifiers
Author
Szczot, Magdalena ; Forster, Julian ; Löhlein, Otto ; Palm, Günther
Author_Institution
Dept. Environ. Perception (GR/PAP), Daimler AG, Ulm, Germany
fYear
2010
fDate
23-26 Aug. 2010
Firstpage
552
Lastpage
555
Abstract
This contribution presents an efficient and useful way to readapt a cascaded classifier. We introduce Package Boosting which combines the advantages of Real Adaboost and Online Boosting for the realization of the strong learners in each cascade layer. We also examine the conditions which need to be fulfilled by a cascade in order to meet the requirements of an online algorithm and present the evaluation results of the system.
Keywords
learning (artificial intelligence); pattern classification; cascaded classifiers; online boosting; package boosting; real Adaboost; Approximation algorithms; Boosting; Classification algorithms; Detectors; Equations; Estimation; Training; Classifier Readaption; Online Boosting;
fLanguage
English
Publisher
ieee
Conference_Titel
Pattern Recognition (ICPR), 2010 20th International Conference on
Conference_Location
Istanbul
ISSN
1051-4651
Print_ISBN
978-1-4244-7542-1
Type
conf
DOI
10.1109/ICPR.2010.140
Filename
5597441
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