URI | http://purl.tuc.gr/dl/dias/90904717-E643-413F-9170-BDE1DA2DBBBD | - |
Αναγνωριστικό | https://link.springer.com/chapter/10.1007%2F978-3-319-46128-1_15 | - |
Αναγνωριστικό | https://doi.org/10.1007/978-3-319-46128-1_15 | - |
Γλώσσα | en | - |
Μέγεθος | 16 pages | en |
Τίτλος | OSLα: Online Structure Learning using background knowledge axiomatization | el |
Δημιουργός | Michelioudakis Evangelos | en |
Δημιουργός | Μιχελιουδακης Ευαγγελος | el |
Δημιουργός | Skarlatidis Anastasios | en |
Δημιουργός | Paliouras, Georgios | en |
Δημιουργός | Artikis, Alexander | en |
Εκδότης | Springer Verlag | en |
Περίληψη | We present OSLα—an online structure learner for Markov Logic Networks (MLNs) that exploits background knowledge axiomatization in order to constrain the space of possible structures. Many domains of interest are characterized by uncertainty and complex relational structure. MLNs is a state-of-the-art Statistical Relational Learning framework that can naturally be applied to domains governed by these characteristics. Learning MLNs from data is challenging, as their relational structure increases the complexity of the learning process. In addition, due to the dynamic nature of many real-world applications, it is desirable to incrementally learn or revise the model’s structure and parameters. Experimental results are presented in activity recognition using a probabilistic variant of the Event Calculus (MLN−EC) as background knowledge and a benchmark dataset for video surveillance. | en |
Τύπος | Πλήρης Δημοσίευση σε Συνέδριο | el |
Τύπος | Conference Full Paper | en |
Άδεια Χρήσης | http://creativecommons.org/licenses/by/4.0/ | en |
Ημερομηνία | 2018-11-13 | - |
Ημερομηνία Δημοσίευσης | 2016 | - |
Θεματική Κατηγορία | Event calculus | en |
Θεματική Κατηγορία | Markov logic networks | en |
Θεματική Κατηγορία | Uncertainty | en |
Βιβλιογραφική Αναφορά | E. Michelioudakis, A. Skarlatidis, G. Paliouras and A. Artikis, "OSLα: Online Structure Learning using background knowledge axiomatization," in Joint European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2016, pp. 232-247. doi: 10.1007/978-3-319-46128-1_15 | el |