Semi-supervised learning for structured output variables

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Semi-supervised learning for structured output variables (2006)

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by Ulf Brefeld , Tobias Scheffer

Venue:	ICML06, 23rd International Conference on Machine Learning
Citations:	27 - 1 self

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@INPROCEEDINGS{Brefeld06semi-supervisedlearning,
    author = {Ulf Brefeld and Tobias Scheffer},
    title = {Semi-supervised learning for structured output variables},
    booktitle = {ICML06, 23rd International Conference on Machine Learning},
    year = {2006}
}

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Abstract

The problem of learning a mapping between input and structured, interdependent output variables covers sequential, spatial, and relational learning as well as predicting recursive structures. Joint feature representations of the input and output variables have paved the way to leveraging discriminative learners such as SVMs to this class of problems. We address the problem of semi-supervised learning in joint input output spaces. The cotraining approach is based on the principle of maximizing the consensus among multiple independent hypotheses; we develop this principle into a semi-supervised support vector learning algorithm for joint input output spaces and arbitrary loss functions. Experiments investigate the benefit of semisupervised structured models in terms of accuracy and F1 score. 1.

Keyphrases

semi-supervised learning structured output variable joint input output space relational learning joint feature representation output variable interdependent output variable multiple independent hypothesis arbitrary loss function discriminative learner recursive structure structured model semi-supervised support vector f1 score

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