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http://dx.doi.org/10.5391/IJFIS.2012.12.2.143

Distance Sensitive AdaBoost using Distance Weight Function  

Lee, Won-Ju (School of Electrical and Electronic Engineering, Yonsei University)
Cheon, Min-Kyu (School of Electrical and Electronic Engineering, Yonsei University)
Hyun, Chang-Ho (Division of Electrical Electronic and Control Engineering, Kongju National University)
Park, Mi-Gnon (School of Electrical and Electronic Engineering, Yonsei University)
Publication Information
International Journal of Fuzzy Logic and Intelligent Systems / v.12, no.2, 2012 , pp. 143-148 More about this Journal
Abstract
This paper proposes a new method to improve performance of AdaBoost by using a distance weight function to increase the accuracy of its machine learning processes. The proposed distance weight algorithm improves classification in areas where the original binary classifier is weak. This paper derives the new algorithm's optimal solution, and it demonstrates how classifier accuracy can be improved using the proposed Distance Sensitive AdaBoost in a simulation experiment of pedestrian detection.
Keywords
Machine Learning; AdaBoost; Distance; Weights;
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