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Probabilistic Inductive Logic Programming [PDF]
Lecture Notes in Computer Science, 2004 Probabilistic inductive logic programming, sometimes also called statistical relational learning, addresses one of the central questions of artificial intelligence: the integration of probabilistic reasoning with first order logic representations and machine learning.
Luc De Raedt +2 more
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Inductive logic programming at 30 [PDF]
Machine Learning, 2021 AbstractInductive logic programming (ILP) is a form of logic-based machine learning. The goal is to induce a hypothesis (a logic program) that generalises given training examples and background knowledge. As ILP turns 30, we review the last decade of research.
Andrew Cropper +2 more
exaly +7 more sources
Knowledge Discovery in Variant Databases Using Inductive Logic Programming [PDF]
Bioinformatics and Biology Insights, 2013 Understanding the effects of genetic variation on the phenotype of an individual is a major goal of biomedical research, especially for the development of diagnostics and effective therapeutic solutions.
Hoan Nguyen +3 more
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Automated identification of protein-ligand interaction features using Inductive Logic Programming: a hexose binding case study [PDF]
BMC Bioinformatics, 2012 Background There is a need for automated methods to learn general features of the interactions of a ligand class with its diverse set of protein receptors. An appropriate machine learning approach is Inductive Logic Programming (ILP), which automatically
A Santos Jose C +4 more
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A History of Probabilistic Inductive Logic Programming [PDF]
Frontiers in Robotics and AI, 2014 The field of Probabilistic Logic Programming (PLP) has seen significant advances in the last 20 years, with many proposals for languages that combine probability with logic programming.
Fabrizio eRiguzzi +2 more
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A logic-based knowledge-driven bidirectional multi-attention GRU framework for fear level classification in humans [PDF]
Scientific ReportsThe importance of fear level detection in affective computing using physiological signals has not been extensively explored. Accurately detecting fear responses facilitates real-time emotional monitoring and improves therapeutic outcomes. The logic-based
Adlene Anusha Joshva +2 more
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A discriminative method for family-based protein remote homology detection that combines inductive logic programming and propositional models [PDF]
BMC Bioinformatics, 2011 Background Remote homology detection is a hard computational problem. Most approaches have trained computational models by using either full protein sequences or multiple sequence alignments (MSA), including all positions.
Carbone Alessandra +2 more
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Rule Learning over Knowledge Graphs: A Review [PDF]
Transactions on Graph Data and Knowledge, 2023 Compared to black-box neural networks, logic rules express explicit knowledge, can provide human-understandable explanations for reasoning processes, and have found their wide application in knowledge graphs and other downstream tasks.
Wu, Hong +4 more
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Extending Coinductive Logic Programming with Co-Facts [PDF]
Electronic Proceedings in Theoretical Computer Science, 2017 We introduce a generalized logic programming paradigm where programs, consisting of facts and rules with the usual syntax, can be enriched by co-facts, which syntactically resemble facts but have a special meaning.
Davide Ancona +2 more
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المنطق الماصدقي: تاريخه وخصائصه وتطبيقاته [PDF]
Maǧallaẗ Kulliyyaẗ Al-ādāb Ǧāmiʿaẗ Būrsaʿīd, 2022 لم يُعرف التمييز بين حدي القضية - المفهوم والماصدق - بشکلٍ انفصالي کلٌ على حدة إلاَّ في وقتٍ متأخر؛ فکل قضية تتکون من حدين هما المفهوم والماصدق، والعلاقة بينهما عکسية کما نعلم؛ کلما زاد المفهوم قل الماصدق والعکس، لکن هذا لا يعني القول بأحدهما فقط دون ...
محمد سيد محمد أبوالعلا
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