Semi-Supervised Sequence Classification with HMMs

@article{Zhong2004SemiSupervisedSC,
  title={Semi-Supervised Sequence Classification with HMMs},
  author={Shi Zhong},
  journal={IJPRAI},
  year={2004},
  volume={19},
  pages={165-182}
}
Using unlabeled data to help supervised learning has become an increasingly attractive methodology and proven to be effective in many applications. This paper applies semi-supervised classification algorithms, based on hidden Markov models, to classify sequences. For model-based classification, semi-supervised learning amounts to using both labeled and unlabeled data to train model parameters. We examine three different strategies of using labeled and unlabeled data in the model training… CONTINUE READING
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Semi-supervised sequence classification with hmms

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