Improving AdaBoost for Classification on Small Training Sample Sets with Active Learning

@inproceedings{Li2003ImprovingAF,
  title={Improving AdaBoost for Classification on Small Training Sample Sets with Active Learning},
  author={Xuchun Li and Lei Wang and Eric Sung},
  year={2003}
}
Recently, AdaBoost has been widely used in many computer vision applications and has shown promising results. However, it is also observed that its classification performance is often poor when the size of the training sample set is small. In certain situations, there may be many unlabelled samples available and labelling them is costly and time-consuming. Thus it is desirable to pick a few good samples to be labelled. The key is how. In this paper, we integrate active learning with AdaBoost to… CONTINUE READING

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