Abstract
In the previous chapters, we discussed the structure learning algorithms for two SRL models and extended them to learn with missing data. In this chapter, we discuss how this algorithm can be adapted to learn to act in sequential domains. We then present three of our most successful applications in real health care data—two cardiovascular prediction problems and the third is prediction of onset of Alzheimer’s disease. We then conclude the chapter with a few NLP applications.
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Dependency paths are paths in the dependency graph between a pair of entities.
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Natarajan, S., Kersting, K., Khot, T., Shavlik, J. (2014). Boosting Statistical Relational Learning in Action. In: Boosted Statistical Relational Learners. SpringerBriefs in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-319-13644-8_6
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DOI: https://doi.org/10.1007/978-3-319-13644-8_6
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