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Boosted Statistical Relational LearnersBoosting in the Presence of Missing Data

Boosted Statistical Relational Learners: Boosting in the Presence of Missing Data [The learning approaches presented in the last two chapters employed the closed-world assumption i.e., whatever that is not observed in the data is assumed to be false. In this chapter, we relax this assumption and derive a boosting algorithm that can effectively work with missing data. The derivation is independent of the model and hence we will discuss about adapting it for RDNs and MLNs. As with other chapters, we will conclude with empirical evaluation on the SRL data sets.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Boosted Statistical Relational LearnersBoosting in the Presence of Missing Data

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Publisher
Springer International Publishing
Copyright
© The Author(s) 2014
ISBN
978-3-319-13643-1
Pages
39 –48
DOI
10.1007/978-3-319-13644-8_5
Publisher site
See Chapter on Publisher Site

Abstract

[The learning approaches presented in the last two chapters employed the closed-world assumption i.e., whatever that is not observed in the data is assumed to be false. In this chapter, we relax this assumption and derive a boosting algorithm that can effectively work with missing data. The derivation is independent of the model and hence we will discuss about adapting it for RDNs and MLNs. As with other chapters, we will conclude with empirical evaluation on the SRL data sets.]

Published: Mar 4, 2015

Keywords: Hide Data; World State; Gradient Step; Functional Gradient; Monte Carlo Expectation Maximization

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