Corpus ID: 14322823

Learning with Positive and Unlabeled Examples Using Weighted Logistic Regression

@inproceedings{Lee2003LearningWP,
  title={Learning with Positive and Unlabeled Examples Using Weighted Logistic Regression},
  author={Wee Sun Lee and B. Liu},
  booktitle={ICML},
  year={2003}
}
The problem of learning with positive and unlabeled examples arises frequently in retrieval applications. We transform the problem into a problem of learning with noise by labeling all unlabeled examples as negative and use a linear function to learn from the noisy examples. To learn a linear function with noise, we perform logistic regression after weighting the examples to handle noise rates of greater than a half. With appropriate regularization, the cost function of the logistic regression… Expand
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