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多分类器集成算法，应用前景广泛，本pdf有益于对该算法的了解和学习，共享之。-multiple classifiers integration algorithm, broad application prospects, the benefits of this algorithm pdf understanding and learning, Sharing.
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Abstract—In recent years, the use of machine learning
algorithms (classifiers) has proven to be of great value in
solving a variety of problems in software engineering including
software faults prediction. This paper extends the idea of
predi
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matlab代码程序，利用Adaboost算法训练人脸图像和非人脸图像，通过迭代得到由多个弱分类器组合而成的强分类器，实现图片里的人脸检测。-Matlab code，Using Adaboost algorithm to train the face images and not face images, obtained strong classifier which is conprised of multiple weak classifiers by iteration ， re
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可以集成多个分类器的投票算法，采用python实现(The voting algorithm of multiple classifiers can be integrated and implemented by python.)
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针对不平衡样本的，可以综合多个分类器的投票算法。(For the unbalanced samples, the voting algorithm of multiple classifiers can be synthesized.)
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