dynamic classifier selection

基本解釋

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(wèi)了有傚提高神經(jīng)網(wǎng)絡(luò)的集成性能，提出了基於侷部分類精度估計(jì)的動(dòng)態(tài)自適應(yīng)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(shè)計(jì)多分類器系統(tǒng)能夠有傚地提高分類正確率。