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| HAL: hal-00512439, version 1 |
| DOI: 10.1007/978-3-642-17316-5 |
| Detailed view |  Export this paper |
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| Advanced Data Mining and Applications - 6th International Conference, ADMA 2010, Chongqing : China (2010) |
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| Discriminative Markov Logic Network Structure Learning based on Propositionalization and chi 2-test |
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| Quang-Thang Dinh 1Matthieu Exbrayat 1 |
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| (2010-11-19) |
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| In this paper we present a bottom-up discriminative algorithm to learn automatically Markov Logic Network structures. Our approach relies on a new propositionalization method that transforms a learning dataset into an approximative representation in the form of boolean tables, from which to construct a set of candidate clauses according to a chi2-test. To compute and choose clauses, we successively use two different optimization criteria, namely pseudo-log-likelihood (PLL) and conditional log-likelihood (CLL), in order to combine the efficiency of PLL optimization algorithms together with the accuracy of CLL ones. First experiments show that our approach outperforms the existing discriminative MLN structure learning algorithms. |
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| 1: | Laboratoire d'Informatique Fondamentale d'Orléans (LIFO) |
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Université d'Orléans : EA4022 – Ecole Nationale Supérieure d'Ingénieurs de Bourges |
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| Subject | : | Computer Science/Learning |
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| Markov Logic Network – Structure Learning – Relational Learning – Propositionalization – Inductive Logic Programming |
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| hal-00512439, version 1 | |
| http://hal.archives-ouvertes.fr/hal-00512439 | |
| oai:hal.archives-ouvertes.fr:hal-00512439 | |
| From: Matthieu Exbrayat | |
| Submitted on: Monday, 30 August 2010 14:35:05 | |
| Updated on: Thursday, 6 January 2011 13:17:19 | |
