AdaBoost, Adaptive Boosting, is a well-known meta machine learning algorithm that was proposed by Yoav Freund and Robert Schapire. In this project there two main files
1. ADABOOST_tr.m
2. ADABOOST_te.m
to traing and test a user-coded learning (classification) algorithm with AdaBoost. A demo file (demo.m) is provided that demonstrates how these two files can be used with a classifier (basic threshold classifier) for two class classification problem.
AdaBoost, Adaptive Boosting, is a well-known meta machine learning algorithm that was proposed by Yoav Freund and Robert Schapire. In this project there two main files
樣板 B 樹 ( B - tree )
規則 :
(1) 每個節點內元素個數在 [MIN,2*MIN] 之間, 但根節點元素個數為 [1,2*MIN]
(2) 節點內元素由小排到大, 元素不重複
(3) 每個節點內的指標個數為元素個數加一
(4) 第 i 個指標所指向的子節點內的所有元素值皆小於父節點的第 i 個元素
(5) B 樹內的所有末端節點深度一樣
