Results 1 to 10 of about 273,034 (301)
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
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''.
Golnoosh Farnadi +5 more
core +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 ...
Ryan A. Rossi +3 more
core +5 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
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 +5 more sources
Statistical Relational Learning for Game Theory
IEEE Transactions on Computational Intelligence and AI in Games, 2015 In this paper, we motivate the use of models and algorithms from the area of Statistical Relational Learning (SRL) as a framework for the description and the analysis of games. SRL combines the powerful formalism of first-order logic with the capability of probabilistic graphical models in handling uncertainty in data and representing dependencies ...
Marco Lippi
openalex +4 more sources
Composition of Sentence Embeddings: Lessons from Statistical Relational Learning [PDF]
Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019), 2019 Camera-ready for *SEM ...
Damien Sileo +3 more
openalex +3 more sources
Statistical Relational Learning for Prognostics [PDF]
, 2012 The field of prognostics aims to predict the remaining useful life of a component or machine by means of probabilistic models. These models typically need to satisfy different requirements imposed by the available data, the expert knowledge and the prediction task at hand.
Jonas Vlasselaer, Wannes Meert
openalex +2 more sources
Statistical Relational Learning with Formal Ontologies [PDF]
, 2009 We propose a learning approach for integrating formal knowledge into statistical inference by exploiting ontologies as a semantically rich and fully formal representation of prior knowledge. The logical constraints deduced from ontologies can be utilized to enhance and control the learning task by enforcing description logic satisfiability in a latent ...
Achim Rettinger +2 more
openalex +3 more sources
Change of representation for statistical relational learning [PDF]
, 2007 Statistical relational learning (SRL) algorithms learn statistical models from relational data, such as that stored in a relational database. We previously introduced view learning for SRL, in which the view of a relational database can be automatically modified, yielding more accurate statistical models.
Jesse Davis +5 more
openalex +2 more sources