Results 11 to 20 of about 273,034 (301)
Statistical Relational Learning: An Inductive Logic Programming Perspective [PDF]
, 2005 In the past few years there has been a lot of work lying at the intersection of probability theory, logic programming and machine learning [14,18,13,9,6,1,11]. This work is known under the names of statistical relational learning [7,5], probabilistic logic learning [4], or probabilistic inductive logic programming.
Luc De Raedt
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Logic, Probability and Learning, or an Introduction to Statistical Relational Learning [PDF]
, 2008 Probabilistic inductive logic programming (PILP), 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
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Transforming Graph Data for Statistical Relational Learning
Journal of Artificial Intelligence Research, 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.
Ryan A. Rossi +3 more
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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
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Statistical relational learning : Structure learning for Markov logic networks
, 2011 A Markov Logic Network is composed of a set of weighted first-order logic formulas. In this dissertation we propose several methods to learn a MLN structure from a relational dataset. These methods are of two kinds: methods based on propositionalization and methods based on Graph of Predicates. The methods based on propositionalization are based on the
Quang-Thang Dinh
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Scalable Statistical Relational Learning for NLP [PDF]
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Tutorial Abstracts, 2016 William Yang Wang, William W. Cohen
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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
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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
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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
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Parameter and Structure Learning Algorithms for Statistical Relational Learning. [PDF]
, 2012 My research activity focuses on the field of Machine Learning. Two key challenges in most machine learning applications are uncertainty and complexity. The standard framework for handling uncertainty is probability, for complexity is first-order logic. Thus we would like to be able to learn and perform inference in representation languages that combine
Elena Bellodi, Fabrizio Riguzzi
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