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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”. In this paper, we define the syntax and semantics of PSL\(^Q\), a new SRL framework that supports reasoning with soft quantifiers, and present its most
Farnadi, Golnoosh +5 more
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Soft quantification in statistical relational learning [PDF]
Machine Learning, 2017 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Farnadi, Golnoosh +4 more
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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 learning (SRL) algorithms to these domains.
Rossi, Ryan A. +3 more
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Bridging Weighted Rules and Graph Random Walks for Statistical Relational Models
Frontiers in Robotics and AI, 2018 The aim of statistical relational learning is to learn statistical models from relational or graph-structured data. Three main statistical relational learning paradigms include weighted rule learning, random walks on graphs, and tensor factorization ...
Seyed Mehran Kazemi, David Poole
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A comparative study of supervised machine learning approaches for slope failure production [PDF]
E3S Web of Conferences, 2021 Over the years, machine learning, which is a well-known method in artificial intelligent (AI) field has become a new trend and extensively applied in various applications to solve a realworld problem. This includes slope failure prediction. Slope failure
Deris Ashanira Mat +2 more
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Symbolic Learning and Reasoning With Noisy Data for Probabilistic Anchoring
Frontiers in Robotics and AI, 2020 Robotic agents should be able to learn from sub-symbolic sensor data and, at the same time, be able to reason about objects and communicate with humans on a symbolic level.
Pedro Zuidberg Dos Martires +5 more
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Statistical relational learning of trust [PDF]
Machine Learning, 2010 The learning of trust and distrust is a crucial aspect of social interaction among autonomous, mentally-opaque agents. In this work, we address the learning of trust based on past observations and context information. We argue that from the truster's point of view trust is best expressed as one of several relations that exist between the agent to be ...
Rettinger, Achim +2 more
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iRASD Journal of Management, 2020 Recently, product development and its performance are the essential elements for the business organization that could enhance the firm performance and attains researchers' intentions. Therefore, the present study examines the impact of human, structural
Ataul Karim Patwary, Fauzan Fauzan
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Neural-Symbolic Argumentation Mining: An Argument in Favor of Deep Learning and Reasoning
Frontiers in Big Data, 2020 Deep learning is bringing remarkable contributions to the field of argumentation mining, but the existing approaches still need to fill the gap toward performing advanced reasoning tasks.
Andrea Galassi +4 more
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Statistical learning is related to early literacy-related skills [PDF]
Reading and Writing, 2014 It has been demonstrated that statistical learning, or the ability to use statistical information to learn the structure of one's environment, plays a role in young children's acquisition of linguistic knowledge. Although most research on statistical learning has focused on language acquisition processes, such as the segmentation of words from fluent ...
Mercedes, Spencer +3 more
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