I run the cross-validation experiment for a given data set, and tried two different approaches: one is based on SVM, another is based on SVM plus Adaboost. But the confusion matrix for two experiments are exactly the same. I am confused on how to explain this kind of result. Adaboost is supposed to start with a weak classifier, but how to determine whether a classifier is weak?
I can answer the second question. A weak classifier for AdaBoost means a classifier with low Variance (and consequently high bias) in Bias-Variance decomposition. The reason is, that combining classifiers increases variance, so when you start with high variance classifiers, you're very likely to overfit.