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Erschienen in:

14.07.2021

Structure learning for relational logistic regression: an ensemble approach

verfasst von: Nandini Ramanan, Gautam Kunapuli, Tushar Khot, Bahare Fatemi, Seyed Mehran Kazemi, David Poole, Kristian Kersting, Sriraam Natarajan

Erschienen in: Data Mining and Knowledge Discovery | Ausgabe 5/2021

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Abstract

We consider the problem of learning Relational Logistic Regression (RLR). Unlike standard logistic regression, the features of RLR are first-order formulae with associated weight vectors instead of scalar weights. We turn the problem of learning RLR to learning these vector-weighted formulae and develop a learning algorithm based on the recently successful functional-gradient boosting methods for probabilistic logic models. We derive the functional gradients and show how weights can be learned simultaneously in an efficient manner. Our empirical evaluation on standard data sets demonstrates the superiority of our approach over other methods for learning RLR.

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We use the term example to mean the grounded target literal. Hence \(y_i = 1\) denotes that the grounding \(\mathsf {Q}(\mathbf {X})=1\) i.e., the grounded target predicate is true. Following standard Bayesian networks terminology, we denote the parents \(\mathcal {A}(\mathsf {Q})\) to include the set of formulae \(\psi \) that influence the current predicate \(\mathsf {Q}\).

We use formulae and clauses interchangeably from hereon.

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Titel: Structure learning for relational logistic regression: an ensemble approach
verfasst von: Nandini Ramanan
Gautam Kunapuli
Tushar Khot
Bahare Fatemi
Seyed Mehran Kazemi
David Poole
Kristian Kersting
Sriraam Natarajan
Publikationsdatum: 14.07.2021
Verlag: Springer US
Erschienen in: Data Mining and Knowledge Discovery / Ausgabe 5/2021
Print ISSN: 1384-5810
Elektronische ISSN: 1573-756X
DOI: https://doi.org/10.1007/s10618-021-00770-8

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