A brief introduction to weakly supervised learning

@article{Zhou2018ABI,
  title={A brief introduction to weakly supervised learning},
  author={Z. Zhou},
  journal={National Science Review},
  year={2018},
  volume={5},
  pages={44-53}
}
  • Z. Zhou
  • Published 2018
  • Computer Science
  • National Science Review
Supervised learning techniques construct predictive models by learning from a large number of training examples, where each training example has a label indicating its ground-truth output. Though current techniques have achieved great success, it is noteworthy that in many tasks it is difficult to get strong supervision information like fully ground-truth labels due to the high cost of the data-labeling process. Thus, it is desirable for machine-learning techniques to work with weak supervision… Expand
423 Citations
Safe semi-supervised learning: a brief introduction
  • 25
  • PDF
Learning from Incomplete and Inaccurate Supervision
  • 10
  • PDF
Towards Safe Weakly Supervised Learning
  • Y. Li, Lan-Zhe Guo, Z. Zhou
  • Computer Science, Medicine
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • 2021
  • 21
  • PDF
Training image classifiers using Semi-Weak Label Data
  • Anxiang Zhang, Ankit Shah, B. Raj
  • Computer Science
  • ArXiv
  • 2021
  • PDF
Learning from Indirect Observations
  • 2
  • PDF
WeakAL: Combining Active Learning and Weak Supervision
  • PDF
Semi-Supervised Partial Label Learning via Confidence-Rated Margin Maximization
  • PDF
Active and Incremental Learning with Weak Supervision
  • 3
  • PDF
Moderately supervised learning: definition and framework
  • PDF
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 106 REFERENCES
Semi-supervised learning by disagreement
  • Z. Zhou, M. Li
  • Computer Science
  • 2008 IEEE International Conference on Granular Computing
  • 2008
  • 182
  • PDF
Convex and scalable weakly labeled SVMs
  • 75
  • PDF
Towards Making Unlabeled Data Never Hurt
  • Y. Li, Z. Zhou
  • Computer Science, Medicine
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • 2015
  • 231
  • PDF
Weak supervision and other non-standard classification problems: A taxonomy
  • 67
Learning From Crowds
  • 1,100
  • PDF
On the relation between multi-instance learning and semi-supervised learning
  • 156
  • PDF
MISSL: multiple-instance semi-supervised learning
  • 134
  • PDF
Learning with Local and Global Consistency
  • 3,606
  • PDF
When semi-supervised learning meets ensemble learning
  • 75
  • PDF
Active Learning Literature Survey
  • 3,850
  • PDF
...
1
2
3
4
5
...