Semi-Supervised Classification Algorithm

A Semi-Supervised Classification Algorithm is a semi-supervised learning algorithm that is a data-driven classification algorithm.

• Context:
  • It can range from being a Semi-Supervised Binary Classification Algorithm to being a Semi-Supervised Multiclass Classification Algorithm.
  • It can be implemented into a Semi-Supervised Classification System (to solve a semi-supervised classification task).
  • …
• Example(s):
  • Progressive Transductive Support Vector Machine (PTSVM).
  • A Semi-Supervised Named Entity Recognition Algorithm.
  • A Semi-Supervised Relation Mention Recognition Algorithm, such as Snowball.
  • …
• Counter-Example(s):
  • a Fully-Supervised Classification Algorithm.
  • a Semi-Supervised Estimation Algorithm, or Semi-Supervised Ranking Algorithm.
• See: Self-Supervised Learning Algorithm.

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References

2008

• (Zhu, 2008) ⇒ Xiaojin Zhu. (2008). “Semi-Supervised Learning Literature Survey (revised edition)." Technical Report 1530, Department of Computer Sciences, University of Wisconsin, Madison.

2007

• (Zhu, 2007) ⇒ Xiaojin Zhu. (2007). “Semi-Supervised Learning." Tutorial at ICML 2007.

2003

• (Chen et al., 2003) ⇒ Yisong Chen, Guoping Wang, and Shihai Dong. (2003). “Learning with Progressive Transductive Support Vector Machine.” In: Pattern Recognition Letters, 24(12). doi:10.1016/S0167-8655(03)00008-4

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