adaboost matlab 程序

Description This a classic AdaBoost implementation, in one single file with easy understandable code. The function consist of two parts a simple weak classifier and a boosting part:  The weak classifier tries to find the best threshold in one of the data dimensions to separate the data into two classes -1 and 1  The boosting part calls the classifier iteratively, after every classification step it changes the weights of miss-classified examples. This creates a cascade of "weak classifiers" which behaves like a "strong classifier"  .  Training mode:    [estimateclass,model]=adaboost("train",datafeatures,dataclass,itt)  Apply mode:   estimateclass=adaboost("apply",datafeatures,model)     inputs/outputs:     datafeatures : An Array with size number_samples x number_features     dataclass : An a

adaboost.m
example.m
license.txt