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Thursday, July 23, 2020

AdaBoost w/ JAVA


 AdaBoost w/ JAVA (Tutorial 01)
 
 prototypeprj.com = zaneacademy.com (version 2.0)

00:06 demo prebuilt version of the application 
01:00 overall classifier classifies all data items correctly after 3 rounds 
02:16 go over the discovered weak classifiers 

04:14 classify a couple of new data items 

05:13 code the application 
05:33 go over the classes in this application 

06:36 setup as JavaFX app. 

07:00 define and pickup application training 
data as commons math library matrices 

09:06 quick introduction to Boosting 

10:21 start with same weight w for all data items 
10:29 logic to be executed at every round 
10:30 sum of weights must add up to 1 
10:36 calculate error rate for each weak classifier 
10:50 pickup weak classifier with the lowest error rate 
10:56 calculate alpha for the picked upweak classifier 
11:03 recalculate the weights w 
11:13 terminate successfully if the overall classifier classifies all data items correctly 

11:29 go over weak classifier functionality while coding TreeTrunk class 

20:57 code AdaBoost class 
21:02 train method will contain logic that is executed at every round 

23:02 method to pickup the best weak classifier 
23:40 method that calculates alpha for the best weak classifier 
24:09 method to recalculate the weights w 

24:29 utility methods for matrix operations 

25:19 calculate the sum of the weighted weak classifiers 

28:30 classify new data item method 

28:50 DisplayHelper code 

29:44 finish coding Driver class 
30:34 JavaFX code 

32:15 test run completed application

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'AdaBoost w/ JAVA (Tutorial 01)'
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