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基於Cascade SVM之平行化AdaBoost分類器之研究
Thesis

基於Cascade SVM之平行化AdaBoost分類器之研究

曾開一
Masters, 國立清華大學, 數學系
2014

Abstract

adaboost 支撐向量機 adaboost support vector machine
In this thesis, we use two algorithms, AdaBoostCascadeSVM.PL and AdaBoostCascadeRVM.PL, which to verify the effect of classification with dynamic adjustment C value, and observe the performance of accuracy and computation time. In AdaBoostCascadeSVM.PL, classification with dynamic adjustment C value can save 22~30 computation time, and receive the similar accuracy when C value equal 25 and 50. On the other hand, the complexity of AdaBoostCascadeRVM.PL is too high to obtain classifier efficiently.

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