AdaBoost w/ JAVA (Tutorial 01) |
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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 ---------------------------------------------------------------------------------------------------------------------------- quickly download, import into Eclipse, and run zip file for 'AdaBoost w/ JAVA (Tutorial 01)' --------------------------------------------------------------------------------------------------------------------------- ----------------------------------------------------------------------------------------------------------- download source code @ https://sites.fastspring.com/prototypeprj/instant/2020
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Thursday, July 23, 2020
AdaBoost w/ JAVA
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