这是一个经典的形变模型实施,在一个单一的文件用简单的可以理解的代码。

功能包括两部分一个简单的弱分类器和一个促进部分:

弱分类器试图找到最佳阈值的数据维数对数据进行分离成两个阶级1和1

要求的进一步提高分类器部分迭代,每一步是变化分类权重miss-classified例子。这造成了一连串的“弱分类器”,表现得像一个“强大分类器”

-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"

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