The challenges of SVM optimization using Adaboost on a phoneme recognition problem
TABLE II: Generalization errors with 3-middle frames of four algorithms per phoneme : SVM-RBF, Adaboost with SVM, Decision Tree C4.5 and Adaboost with Decision Tree C4.5
['[BOLD] Classifiers', '[BOLD] SVM', '[BOLD] AdaboostSVM', '[BOLD] C4.5', '[BOLD] AdaboostC4.5']
We observe that the AdaboostSVM performs similarly or worse than the single SVM-RBF. We think that forcing the strong SVM classifiers (SVM with its best parameters) to concentrate on the very hard samples with too much emphasis is the cause of performance degradation in AdaboostSVM algorithm. Furthermore, for SVM we set a small value of γ and also the most suitable for our data (γ=0.008), which make the SVM classifier stronger. Thus, Adaboost become inefficient because the errors of these component classifiers are highly correlated. Hence, the use of a suitable gamma for SVM component classifier un Adaboost leads to lower the performance of AdaboostSVM for phoneme recognition.