Semi-supervised Learning for SVM-KNN

@article{Li2010SemisupervisedLF,
  title={Semi-supervised Learning for SVM-KNN},
  author={Kunlun Li and Xuerong Luo and Ming Jin},
  journal={JCP},
  year={2010},
  volume={5},
  pages={671-678}
}
Compared with labeled data, unlabeled data are significantly easier to obtain. Currently, classification of unlabeled data is an open issue. In this paper a novel SVM-KNN classification methodology based on Semi-supervised learning is proposed, we consider the problem of using a large number of unlabeled data to boost performance of the classifier when only a small set of labeled examples is available. We use the few labeled data to train a weaker SVM classifier and make use of the boundary… CONTINUE READING

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References

Publications referenced by this paper.
SHOWING 1-10 OF 21 REFERENCES

Supervised Learning [M

Olivier Chapelle, Bernhard Schölkopf, Alexander Zien. Semi
  • 2006
VIEW 3 EXCERPTS
HIGHLY INFLUENTIAL

Semi-supervised learning literature survey[R

X. J. Zhu
  • Technical Report 1530, Department of Computer Sciences, University of Wisconsin at Madison, Madison, WI, December
  • 2007
VIEW 2 EXCERPTS

Semi-supervised learning with very few labeled training examples

Zhou, Z.-H, Zhan, D.-C, Q. Yang
  • Twenty- Second AAAI Conference on Artificial Intelligence
  • 2007
VIEW 2 EXCERPTS

A study on sigmoid kernels for SVM and the training of non-PSD kernels by SMO-type methods

Lin, H.-T, C.-J
  • Technical report,
  • 2003
VIEW 2 EXCERPTS

Asymptotic behaviors of support vector machines with Gaussian kernel

S. S. Keerthi, C.-J
  • Neural Computation
  • 2003
VIEW 1 EXCERPT