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• W2137450504 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 co-training 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 semi-supervised structured models in terms of accuracy and F1 score." @default.
• W2137450504 created "2016-06-24" @default.
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• W2137450504 creator A5049583553 @default.
• W2137450504 date "2006-01-01" @default.
• W2137450504 modified "2023-10-02" @default.
• W2137450504 title "Semi-supervised learning for structured output variables" @default.
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• W2137450504 doi "https://doi.org/10.1145/1143844.1143863" @default.
• W2137450504 hasPublicationYear "2006" @default.
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