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 +4 more sources
Score As You Lift (SAYL): A Statistical Relational Learning Approach to Uplift Modeling. [PDF]
Mach Learn Knowl Discov Databases, 2013 Nassif H +5 more
europepmc +3 more sources
Predicting virus mutations through statistical relational learning. [PDF]
BMC Bioinformatics, 2014 Cilia E +4 more
europepmc +3 more sources
Effect of powder composition, PTAW parameters on dilution, microstructure and hardness of Ni–Cr–Si–B alloy deposition: Experimental investigation and prediction using machine learning technique [PDF]
HeliyonThe implementation of hard-facing alloy on the existing materials caters the need for high-performance surfaces in terms of wear and high temperatures. The present research explore the effect of Plasma Transferred Arc Welding (PTAW) parameters and powder
Venkatesh Chenrayan +6 more
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
Technological capability, relational capability and firms’ performance: The role of learning capability [PDF]
REGE Revista de Gestão, 2020 Purpose – The purpose of this paper is to empirically evaluate the mediating role of learning capability on the relationship between technological capability, relational capability and small and medium enterprises (SMEs) performance in developing economy
Yakubu Salisu, Lily Julienti Abu Bakar
doaj +1 more source
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
doaj +1 more source