{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T14:25:54Z","timestamp":1779287154084,"version":"3.51.4"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2015,2,20]],"date-time":"2015-02-20T00:00:00Z","timestamp":1424390400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Mach Learn"],"published-print":{"date-parts":[[2015,7]]},"DOI":"10.1007\/s10994-015-5481-4","type":"journal-article","created":{"date-parts":[[2015,2,19]],"date-time":"2015-02-19T20:53:52Z","timestamp":1424379232000},"page":"75-100","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Gradient-based boosting for statistical relational learning: the Markov logic network and missing data cases"],"prefix":"10.1007","volume":"100","author":[{"given":"Tushar","family":"Khot","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sriraam","family":"Natarajan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kristian","family":"Kersting","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jude","family":"Shavlik","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2015,2,20]]},"reference":[{"key":"5481_CR1","doi-asserted-by":"crossref","unstructured":"Bellodi, E., & Riguzzi, F. (2012). Learning the structure of probabilistic logic programs. In Inductive logic programming (ILP), LNCS (Vol. 7207, pp 61\u201375).","DOI":"10.1007\/978-3-642-31951-8_10"},{"issue":"2","key":"5481_CR2","doi-asserted-by":"crossref","first-page":"343","DOI":"10.3233\/IDA-130582","volume":"17","author":"E Bellodi","year":"2013","unstructured":"Bellodi, E., & Riguzzi, F. (2013). Expectation maximization over binary decision diagrams for probabilistic logic programs. Intelligent Data Analysis, 17(2), 343\u2013363.","journal-title":"Intelligent Data Analysis"},{"issue":"3","key":"5481_CR3","doi-asserted-by":"crossref","first-page":"179","DOI":"10.2307\/2987782","volume":"24","author":"J Besag","year":"1975","unstructured":"Besag, J. (1975). Statistical analysis of non-lattice data. The Statistician, 24(3), 179\u2013195.","journal-title":"The Statistician"},{"key":"5481_CR4","unstructured":"Biba, M., Ferilli, S., & Esposito, F. (2008). Structure learning of Markov logic networks through iterated local search. In European conference on artificial intelligence (ECAI) (pp. 361\u2013365)."},{"key":"5481_CR5","doi-asserted-by":"crossref","unstructured":"Blockeel, H., & De Raedt, L. (1998). Top\u2013down induction of first-order logical decision trees. Artificial Intelligence, 101, 285\u2013297.","DOI":"10.1016\/S0004-3702(98)00034-4"},{"key":"5481_CR6","unstructured":"Chan, P., & Stolfo, S. (1998). Toward scalable learning with non-uniform class and cost distributions: A case study in credit card fraud detection. In Knowledge discovery and data mining (KDD) (pp. 164\u2013168)."},{"key":"5481_CR7","unstructured":"Craven, M., DiPasquo, D., Freitag, D., McCallum, A., Mitchell, T., Nigam, K., & Slattery, S. (1998). Learning to extract symbolic knowledge from the World Wide Web. In Association for the advancement of artificial intelligence conference (AAAI) (pp. 509\u2013516)."},{"key":"5481_CR8","doi-asserted-by":"crossref","unstructured":"Davis, J., & Goadrich, M. (2006). The relationship between Precision-Recall and ROC curves. In International conference on machine learning (ICML) (pp 233\u2013240).","DOI":"10.1145\/1143844.1143874"},{"key":"5481_CR9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.2517-6161.1977.tb01600.x","volume":"39","author":"A Dempster","year":"1977","unstructured":"Dempster, A., Laird, N., & Rubin, D. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society B., 39, 1\u201338.","journal-title":"Journal of the Royal Statistical Society B."},{"key":"5481_CR10","unstructured":"Dietterich, T., Ashenfelter, A., & Bulatov, Y. (2008). Gradient tree boosting for training conditional random fields. Journal of Machine Learning Research, 9, 2113\u20132139."},{"key":"5481_CR11","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-031-01549-6","volume-title":"Markov logic: An interface layer for AI","author":"P Domingos","year":"2009","unstructured":"Domingos, P., & Lowd, D. (2009). Markov logic: An interface layer for AI. San Rafael: Morgan & Claypool."},{"key":"5481_CR12","doi-asserted-by":"crossref","unstructured":"Friedman, J. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189\u20131232.","DOI":"10.1214\/aos\/1013203451"},{"key":"5481_CR13","unstructured":"Friedman, N. (1998). The Bayesian structural EM algorithm. In Uncertainty in artificial intelligence (UAI) (pp. 129\u2013138)."},{"key":"5481_CR14","doi-asserted-by":"crossref","unstructured":"Getoor, L., & Taskar, B. (eds) (2007). Introduction to statistical relational learning. Cambridge: MIT Press.","DOI":"10.7551\/mitpress\/7432.001.0001"},{"key":"5481_CR15","doi-asserted-by":"crossref","unstructured":"Getoor, L., Friedman, N., Koller, D., & Pfeffer, A. (2001). Learning probabilistic relational models. In: S. Dzeroski & N. Lavra\u010d (Eds.), Relational data mining (pp. 307\u2013338). Berlin, Heidelberg: Springer.","DOI":"10.1007\/978-3-662-04599-2_13"},{"key":"5481_CR16","doi-asserted-by":"crossref","unstructured":"Gutmann, B., Thon, I., & Raedt, LD. (2011). Learning the parameters of probabilistic logic programs from interpretations. In European conference on machine learning (ECML) (pp. 581\u2013596).","DOI":"10.1007\/978-3-642-23780-5_47"},{"key":"5481_CR17","doi-asserted-by":"crossref","unstructured":"Jaeger, M. (2007). Parameter learning for relational Bayesian networks. In International conference on machine learning (ICML) (pp. 369\u2013376).","DOI":"10.1145\/1273496.1273543"},{"key":"5481_CR18","doi-asserted-by":"crossref","unstructured":"Kameya, Y., & Sato, T. (2000). Efficient EM learning with tabulation for parameterized logic programs. In Computational logic (CL) (pp. 269\u2013284).","DOI":"10.1007\/3-540-44957-4_18"},{"key":"5481_CR19","doi-asserted-by":"crossref","unstructured":"Karwath, A., Kersting, K., & Landwehr, N. (2008). Boosting relational sequence alignments. In IEEE international conference on data mining (ICDM) (pp. 857\u2013862).","DOI":"10.1109\/ICDM.2008.127"},{"key":"5481_CR20","doi-asserted-by":"crossref","unstructured":"Kersting, K., & De Raedt, L. (2007). Bayesian logic programming: Theory and tool. In An introduction to statistical relational learning (pp. 291\u2013322).","DOI":"10.7551\/mitpress\/7432.003.0012"},{"key":"5481_CR21","doi-asserted-by":"crossref","unstructured":"Kersting, K., & Driessens, K. (2008). Non-parametric policy gradients: A unified treatment of propositional and relational domains. In International conference on machine learning (ICML) (pp. 456\u2013463).","DOI":"10.1145\/1390156.1390214"},{"key":"5481_CR22","unstructured":"Kersting, K., & Raiko, T. (2005). \u2018Say EM\u2019 for selecting probabilistic models for logical sequences. In Uncertainty in artificial intelligence (UAI) (pp. 300\u2013307)."},{"key":"5481_CR23","unstructured":"Kersting, K., Ahmadi, B., & Natarajan, S. (2009). Counting belief propagation. In Uncertainty in artificial intelligence (UAI) (pp. 277\u2013284)."},{"key":"5481_CR24","doi-asserted-by":"crossref","unstructured":"Khot, T., Natarajan, S., Kersting, K., & Shavlik, J. (2011) . Learning Markov logic networks via functional gradient boosting. In IEEE international conference on data mining (ICDM) (pp. 320\u2013329).","DOI":"10.1109\/ICDM.2011.87"},{"key":"5481_CR25","doi-asserted-by":"crossref","unstructured":"Kok, S., & Domingos, P. (2009). Learning Markov logic network structure via hypergraph lifting. In International conference on machine learning (ICML) (pp. 505\u2013512).","DOI":"10.1145\/1553374.1553440"},{"key":"5481_CR26","unstructured":"Kok, S., & Domingos, P. (2010). Learning Markov logic networks using structural motifs. In International conference on machine learning (ICML) (pp. 551\u2013558)."},{"key":"5481_CR27","unstructured":"Kok, S., Sumner, M., Richardson, M., Singla, P., Poon, H., Lowd, D., Wang, J., Nath, A., & Domingos, P. (2010). The Alchemy system for statistical relational AI. Technical report, Department of Computer Science and Engineering, University of Washington, Seattle, WA. http:\/\/alchemy.cs.washington.edu"},{"key":"5481_CR28","doi-asserted-by":"crossref","unstructured":"Li, X., & Zhou, Z. (2007). Structure learning of probabilistic relational models from incomplete relational data. In European conference on machine learning (ECML) (pp. 214\u2013225).","DOI":"10.1007\/978-3-540-74958-5_22"},{"key":"5481_CR29","doi-asserted-by":"crossref","unstructured":"Mihalkova, L., & Mooney, R. (2007). Bottom-up learning of Markov logic network structure. In International conference on machine learning (ICML) (pp. 625\u2013632).","DOI":"10.1145\/1273496.1273575"},{"issue":"1\u20133","key":"5481_CR30","first-page":"223","volume":"54","author":"S Natarajan","year":"2008","unstructured":"Natarajan, S., Tadepalli, P., Dietterich, T., & Fern, A. (2008). Learning first-order probabilistic models with combining rules. Annals of Mathematics and AI, 54(1\u20133), 223\u2013256.","journal-title":"Annals of Mathematics and AI"},{"key":"5481_CR31","unstructured":"Natarajan, S., Joshi, S., Tadepalli, P., Kristian, K., & Shavlik, J. (2011). Imitation learning in relational domains: A functional-gradient boosting approach. In International joint conference on artificial intelligence (IJCAI) (pp. 1414\u20131420)."},{"issue":"1","key":"5481_CR32","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1007\/s10994-011-5244-9","volume":"86","author":"S Natarajan","year":"2012","unstructured":"Natarajan, S., Khot, T., Kersting, K., Guttmann, B., & Shavlik, J. (2012). Gradient-based boosting for statistical relational learning: The relational dependency network case. Machine Learning, 86(1), 25\u201356.","journal-title":"Machine Learning"},{"key":"5481_CR33","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1007\/s10994-006-5833-1","volume":"62","author":"M Richardson","year":"2006","unstructured":"Richardson, M., & Domingos, P. (2006). Markov logic networks. Machine Learning, 62, 107\u2013136.","journal-title":"Machine Learning"},{"key":"5481_CR34","unstructured":"Russell, S., & Norvig, P. (2003). Artificial intelligence: A modern approach. Englewood Cliffs, NJ: Pearson Education."},{"key":"5481_CR35","unstructured":"Shavlik, J., & Natarajan, S. (2009). Speeding up inference in Markov logic networks by preprocessing to reduce the size of the resulting grounded network. In International joint conference on artificial intelligence (IJCAI) (pp. 1951\u20131956)."},{"key":"5481_CR36","unstructured":"Singla, P., & Domingos, P. (2008). Lifted first-order belief propagation. In Association for the advancement of artificial intelligence conference (AAAI) (pp. 1094\u20131099)."},{"key":"5481_CR37","doi-asserted-by":"crossref","unstructured":"Xiang, R., & Neville, J. (2008). Pseudolikelihood EM for within-network relational learning. In IEEE international conference on data mining (ICDM) (pp. 1103\u20131108).","DOI":"10.1109\/ICDM.2008.148"}],"container-title":["Machine Learning"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-015-5481-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10994-015-5481-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-015-5481-4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,19]],"date-time":"2025-05-19T19:36:00Z","timestamp":1747683360000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10994-015-5481-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,2,20]]},"references-count":37,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2015,7]]}},"alternative-id":["5481"],"URL":"https:\/\/doi.org\/10.1007\/s10994-015-5481-4","relation":{},"ISSN":["0885-6125","1573-0565"],"issn-type":[{"value":"0885-6125","type":"print"},{"value":"1573-0565","type":"electronic"}],"subject":[],"published":{"date-parts":[[2015,2,20]]}}}