dynamic classifier selection

基本解释

Based on Bayesian theory, it can be proved that the performance of the dynamic adaptive selection ensemble can approximate the optimal Bayesian classifier if certain hypotheses are met.
爲了有傚提高神經網絡的集成性能，提出了基於侷部分類精度估計的動態自適應選擇集成的思想。
However, the only existing classifier combination method which uses local accuracy, namely dynamic classifier selection, can not take full advantage of the information from the base classifiers.
基於侷部分類精度設計多分類器系統能夠有傚地提高分類正確率。