ML Lecture 22: Ensemble

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The video recorded at the spring of 2017 does not have the "pointer", so I upload this version.
  1. Hung-yi Lee
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Комментарии
Автор

想問一些問題
1. 如果把Boosting 放在Strong Classifier 上面, 會不會帶來更好的效果 還是只是帶來overfitting？

2. Boosting 是不是可以處理樣本數量不平均得問題上面? (比如True label 太多, False label 太少的問題上) 
由於True label 太多, classifier 隨便猜True 他的accuracy 都很高或是都只學習到 True label的feature, 而忽視了False label的feature. 是否可以藉由boosting 來學習少量False label dataset的feature特徵？

3. 在Bagging 得f(x)裡面, 我們是不是應該要選不同的model (SVM, NN, Tree...etc.) ? 如果我四個model 都是 neural network 但只是hyper-parameter (learning rate, hidden node, hidden layer)不同, 是不是失去Bagging 的意義了?

求解
謝謝!😭

葉柏宏-mt
Автор

老师·，我看您最后一页有提到Deep Learning for Image Processing(暂定)
请问最近有计划制作这门课程吗？

蒋志强-dh
Автор

1:06:51 I guess all the subscript on the right hand side should be t-1, including Z, epsilon and alpha?

ysmashimaro
Автор

Hall Karen Allen Cynthia Jackson Scott

ИсидораАлександрова