Logistic Boosting Regression for Label Distribution Learning

@article{Xing2016LogisticBR,
  title={Logistic Boosting Regression for Label Distribution Learning},
  author={Chao Xing and Xin Geng and Hui Xue},
  journal={2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2016},
  pages={4489-4497}
}
Label Distribution Learning (LDL) is a general learning framework which includes both single label and multi-label learning as its special cases. One of the main assumptions made in traditional LDL algorithms is the derivation of the parametric model as the maximum entropy model. While it is a reasonable assumption without additional information, there is no particular evidence supporting it in the problem of LDL. Alternatively, using a general LDL model family to approximate this parametric… CONTINUE READING

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