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Statistical Relational Learning With Unconventional String Models [PDF]
Frontiers in Robotics and AI, 2018 This paper shows how methods from statistical relational learning can be used to address problems in grammatical inference using model-theoretic representations of strings.
Mai H. Vu +5 more
doaj +5 more sources
Soft quantification in statistical relational learning [PDF]
Machine Learning, 2017 We present a new statistical relational learning (SRL) framework that supports reasoning with soft quantifiers, such as "most" and "a few." We define the syntax and the semantics of this language, which we call , and present a most probable explanation ...
A Charnes +19 more
core +7 more sources
Statistical relational learning with soft quantifiers [PDF]
, 2016 Quantification in statistical relational learning (SRL) is either existential or universal, however humans might be more inclined to express knowledge using soft quantifiers, such as ``most'' and ``a few''.
B Huang +16 more
core +4 more sources
Predicting virus mutations through statistical relational learning. [PDF]
BMC Bioinformatics, 2014 Abstract Background Viruses are typically characterized by high mutation rates, which allow them to quickly develop drug-resistant mutations. Mining relevant rules from mutation data can be extremely useful to understand the virus adaptation mechanism and to design drugs that effectively counter potentially resistant ...
Cilia E +4 more
europepmc +7 more sources
Transforming Graph Representations for Statistical Relational Learning [PDF]
, 2012 Relational data representations have become an increasingly important topic due to the recent proliferation of network datasets (e.g., social, biological, information networks) and a corresponding increase in the application of statistical relational ...
Aha, David W. +3 more
core +2 more sources
A causal discovery-based adaptive fusion algorithm for multi-source heterogeneous knowledge graphs [PDF]
Scientific ReportsMulti-source heterogeneous knowledge graph fusion faces significant challenges due to schema heterogeneity, entity conflicts, and relationship inconsistencies across different knowledge sources.
Ting Wang
doaj +2 more sources
Relational agency and relational people management: evidence from Uganda’s micro and small enterprises [PDF]
Asia Pacific Journal of Innovation and Entrepreneurship, 2022 Purpose – This paper aims to investigate whether relational agency fosters relational people management using evidence from micro and small enterprises in Uganda, an African developing country.
Grace Nalweyiso +5 more
doaj +1 more source
Learning and reasoning with graph data
Frontiers in Artificial Intelligence, 2023 Reasoning about graphs, and learning from graph data is a field of artificial intelligence that has recently received much attention in the machine learning areas of graph representation learning and graph neural networks.
Manfred Jaeger
doaj +1 more source
Integration of grey analysis with artificial neural network for classification of slope failure [PDF]
E3S Web of Conferences, 2021 With the advent of technology and the introduction of computational intelligent methods, the prediction of slope failure using the machine learning (ML) approach is rapidly growing for the past few decades.
Deris Ashanira Mat +2 more
doaj +1 more source
Logic + probabilistic programming + causal laws
Royal Society Open Science, 2023 Probabilistic planning attempts to incorporate stochastic models directly into the planning process, which is the problem of synthesizing a sequence of actions that achieves some objective for a putative agent.
Vaishak Belle
doaj +1 more source