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Predicting Preterm Birth Is Not Elusive: Machine Learning Paves the Way to Individual Wellness.

, , , , , , , and . AAAI Spring Symposia, AAAI Press, (2014)

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Machine Learning for theNew York City Power Grid., , , , , , , , , and . IEEE Trans. Pattern Anal. Mach. Intell., 34 (2): 328-345 (2012)Alive on Back-feed Culprit Identification via Machine Learning., , and . ICMLA, page 725-730. IEEE Computer Society, (2009)Vers des Machines à Vecteurs Support Äctionnables" : Une Approche Fondée sur le Classement., , and . EGC, volume RNTI-E-11 of Revue des Nouvelles Technologies de l'Information, page 285-296. Cépaduès-Éditions, (2008)Application of Sentiment and Topic Analysis to Teacher Evaluation Policy in the U.S., , and . EDM, page 628-629. International Educational Data Mining Society (IEDMS), (2015)Making Sense of Word Sense Variation., , and . SEW@NAACL-HLT, page 2-9. Association for Computational Linguistics, (2009)Sequential event prediction with association rules, , , , and . (2011)QuantMiner: A Genetic Algorithm for Mining Quantitative Association Rules., , and . IJCAI, page 1035-1040. (2007)Word Sense Annotation of Polysemous Words by Multiple Annotators., , , and . LREC, European Language Resources Association, (2010)Learning Characteristic Rules in Geographic Information Systems., , and . RuleML, volume 9202 of Lecture Notes in Computer Science, page 431-444. Springer, (2015)Automated Symbolic Law Discovery: A Computer Vision Approach., , and . AAAI, page 660-668. AAAI Press, (2021)