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(shù) ( B - tree )
規(guī)則 :
(1) 每個(gè)節(jié)點(diǎn)內(nèi)元素個(gè)數(shù)在 [MIN,2*MIN] 之間, 但根節(jié)點(diǎn)元素個(gè)數(shù)為 [1,2*MIN]
(2) 節(jié)點(diǎn)內(nèi)元素由小排到大, 元素不重複
(3) 每個(gè)節(jié)點(diǎn)內(nèi)的指標(biāo)個(gè)數(shù)為元素個(gè)數(shù)加一
(4) 第 i 個(gè)指標(biāo)所指向的子節(jié)點(diǎn)內(nèi)的所有元素值皆小於父節(jié)點(diǎn)的第 i 個(gè)元素
(5) B 樹(shù)內(nèi)的所有末端節(jié)點(diǎn)深度一樣
