کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
534466 870254 2010 11 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
On selection and combination of weak learners in AdaBoost
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
پیش نمایش صفحه اول مقاله
On selection and combination of weak learners in AdaBoost
چکیده انگلیسی

Despite of its great success, two key problems are still unresolved for AdaBoost algorithms: how to select the most discriminative weak learners and how to optimally combine them. In this paper, a new AdaBoost algorithm is proposed to make improvement in the two aspects. First, we select the most discriminative weak learners by minimizing a novel distance related criterion, i.e., error-degree-weighted training error metric (ETEM) together with generalization capability metric (GCM), rather than training error rate only. Second, after getting the coefficients that are set empirically, we combine the weak learners optimally by tuning the coefficients using kernel-based perceptron. Experiments with synthetic and real scene data sets show our algorithm outperforms conventional AdaBoost.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition Letters - Volume 31, Issue 9, 1 July 2010, Pages 991–1001
نویسندگان
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