caa a22 4500 60547818X CHVBK 20210128100404.0 cr unu---uuuuu 210128e20150701xx s 000 0 eng 10.1007/s10994-015-5481-4 doi (NATIONALLICENCE)springer-10.1007/s10994-015-5481-4 Gradient-based boosting for statistical relational learning: the Markov logic network and missing data cases [Elektronische Daten] [Tushar Khot, Sriraam Natarajan, Kristian Kersting, Jude Shavlik] Recent years have seen a surge of interest in Statistical Relational Learning (SRL) models that combine logic with probabilities. One prominent and highly expressive SRL model is Markov Logic Networks (MLNs), but the expressivity comes at the cost of learning complexity. Most of the current methods for learning MLN structure follow a two-step approach where first they search through the space of possible clauses (i.e. structures) and then learn weights via gradient descent for these clauses. We present a functional-gradient boosting algorithm to learn both the weights (in closed form) and the structure of the MLN simultaneously. Moreover most of the learning approaches for SRL apply the closed-world assumption, i.e., whatever is not observed is assumed to be false in the world. We attempt to open this assumption. We extend our algorithm for MLN structure learning to handle missing data by using an EM-based approach and show this algorithm can also be used to learn Relational Dependency Networks and relational policies. Our results in many domains demonstrate that our approach can effectively learn MLNs even in the presence of missing data. The Author(s), 2015 Statistical relational learning nationallicence Markov logic networks nationallicence Missing data nationallicence Expectation maximization nationallicence Khot Tushar University of Wisconsin, Madison, WI, USA aut Natarajan Sriraam Indiana University, Bloomington, IN, USA aut Kersting Kristian TU Dortmund University, Dortmund, Germany aut Shavlik Jude University of Wisconsin, Madison, WI, USA aut Machine Learning Springer US; http://www.springer-ny.com 100/1(2015-07-01), 75-100 0885-6125 100:1<75 2015 100 10994 https://doi.org/10.1007/s10994-015-5481-4 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s10994-015-5481-4 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Khot Tushar University of Wisconsin, Madison, WI, USA aut NATIONALLICENCE 700 1- Natarajan Sriraam Indiana University, Bloomington, IN, USA aut NATIONALLICENCE 700 1- Kersting Kristian TU Dortmund University, Dortmund, Germany aut NATIONALLICENCE 700 1- Shavlik Jude University of Wisconsin, Madison, WI, USA aut NATIONALLICENCE 773 0- Machine Learning Springer US; http://www.springer-ny.com 100/1(2015-07-01), 75-100 0885-6125 100:1<75 2015 100 10994