bagging是一種用來提高學習算法準確度的方法,這種方法通過構造一個預測函數係列,然後以一定的方式將它們組合成一個預測函數。Bagging要求“不穩定”(不穩定是指數據集的小的變動能夠使得分類結果的顯著的變動)的分類方法。比如:決策樹,神經網絡算法。

最新論文

In this paper, we propose a new statistical inference method for massive data sets, which is very simple and efficient by combining divide-and-conquer method and empirical likelihood. Compared with two popular methods (the bag of little bootstrap and the subsampled double bootstrap), we make full use of data sets, and reduce the computation burden. Extensive numerical studies and real data analysis demonstrate the effectiveness and flexibility of our proposed method. Furthermore, the asymptotic property of our method is derived.

0
0
0
下載
預覽
父主題
Top