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Statistical relational learning
Known as:
SRL
, PRM
, Probabilistic relational model
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Statistical relational learning (SRL) is a subdiscipline of artificial intelligence and machine learning that is concerned with domain models that…
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Related topics
Related topics
23 relations
Artificial intelligence
Association rule learning
Bayesian network
Cluster analysis
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Broader (2)
Computational statistics
Machine learning
Papers overview
Semantic Scholar uses AI to extract papers important to this topic.
2017
2017
Soft quantification in statistical relational learning
G. Farnadi
,
Stephen H. Bach
,
Marie-Francine Moens
,
L. Getoor
,
M. D. Cock
Machine-mediated learning
2017
Corpus ID: 43407720
We present a new statistical relational learning (SRL) framework that supports reasoning with soft quantifiers, such as “most…
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2015
2015
Industrial system knowledge formalization to aid decision making in maintenance strategies assessment
Gabriela Medina-Oliva
,
P. Weber
,
B. Iung
Engineering applications of artificial…
2015
Corpus ID: 22244594
Review
2014
Review
2014
Statistical Relational Learning
S. Natarajan
,
Kristian Kersting
,
Tushar Khot
,
J. Shavlik
2014
Corpus ID: 267830669
This chapter presents background on SRL models on which our work is based on. We start with a brief technical background on first…
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Review
2013
Review
2013
Statistical Relational Learning
H. Blockeel
Handbook on Neural Information Processing
2013
Corpus ID: 34343661
Relational learning refers to learning from data that have a complex structure. This structure may be either internal (a data…
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Review
2009
Review
2009
The panicle rice mite, Steneotarsonemus spinki Smiley, a re-discovered pest of rice in the United States
N. Hummel
,
B. Castro
,
Eric M. McDonald
,
Miguel A. Pellerano
,
R. Ochoa
2009
Corpus ID: 21111687
2008
2008
A statistical relational model for trust learning
Achim Rettinger
,
Matthias Nickles
,
Volker Tresp
Adaptive Agents and Multi-Agent Systems
2008
Corpus ID: 14930411
We address the learning of trust based on past observations and context information. We argue that from the truster's point of…
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2008
2008
Improving the Performance of Sampling-Based Motion Planning With Symmetry-Based Gap Reduction
P. Cheng
,
Emilio Frazzoli
,
S. LaValle
IEEE Transactions on robotics
2008
Corpus ID: 767929
Sampling-based nonholonomic and kinodynamic planning iteratively constructs solutions with sampled controls. A constructed…
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2003
2003
Statistical Relational Learning at U Penn
Alexandrin Popescul
,
L. Ungar
2003
Corpus ID: 60597141
The methyl, ethyl, n-propy, 2-(acetylamino)ethyl, or 1-(2,3-dihydroxy)propyl ester of E-(3R,5S)-7-(4'-fluoro-3,3',5-trimethyl[1,1…
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2003
2003
A general framework for PRM motion planning
Guang Song
,
Shawna L. Thomas
,
N. Amato
IEEE International Conference on Robotics and…
2003
Corpus ID: 2197850
An important property of PRM roadmaps is that they provide a good approximation of the connectivity of the free C-space. We…
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Highly Cited
2001
Highly Cited
2001
Customizing PRM roadmaps at query time
Guang Song
,
Shawna Miller
,
N. Amato
Proceedings ICRA. IEEE International Conference…
2001
Corpus ID: 12232264
We propose an approach for building and querying probabilistic roadmaps. In the roadmap construction stage, we build coarse…
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