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Langseth, Helge Author

Analysis of OREDA data for maintenance optimisation

  • Langseth H.
  • Haugen K.
  • Sandtorv H.

Reliability Engineering and System Safety - 1/1/1998

10.1016/s0951-8320(98)83003-2

Cite count: 18 (Scopus)
  • CiteScore: 9.3 (2020)

Uncertainty bounds for a monotone multistate system

  • Langseth H.
  • Lindqvist B.

Probability in the Engineering and Informational Sciences - 1/12/1998

10.1017/s0269964800005179

Cite count: 10 (Scopus)
  • CiteScore: 1.7 (2020)

Parameter learning in object-oriented Bayesian networks

  • Langseth H.
  • Bangsø O.

Annals of Mathematics and Artificial Intelligence - 1/12/2001

10.1023/a:1016769618900

Cite count: 27 (Scopus)
  • SJR Quartile: Q2
  • CiteScore: 2.5 (2020)
  • SJR: 0.747 (2001
  • SNIP: 1.542 (2001
  • SJR Categories: Applied Mathematics (Q2); Artificial Intelligence (Q2)

The SACSO methodology for troubleshooting complex systems

  • Jensen F.
  • Kjærulff U.
  • Kristiansen B.
  • Langseth H.
  • Skaanning C.
  • Vomlel J.
  • Vomlelová M.
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Artificial Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM - 1/9/2001

10.1017/s0890060401154065

Cite count: 55 (Scopus)
  • SJR Quartile: Q1
  • CiteScore: 2.9 (2020)
  • SJR: 0.365 (2001
  • SNIP: 0.885 (2001
  • SJR Categories: Industrial and Manufacturing Engineering (Q1); Artificial Intelligence (Q3)

Decision theoretic troubleshooting of coherent systems

  • Langseth H.
  • Jensen F.

Reliability Engineering and System Safety - 1/4/2003

10.1016/s0951-8320(02)00202-8

Cite count: 25 (Scopus)
  • SJR Quartile: Q1
  • CiteScore: 9.3 (2020)
  • SJR: 0.414 (2003
  • SNIP: 1.307 (2003
  • SJR Categories: Industrial and Manufacturing Engineering (Q1); Safety, Risk, Reliability and Quality (Q2); Applied Mathematics (Q3)
Open Access

Fusion of domain knowledge with data for structural learning in object oriented domains

  • Langseth H.
  • Nielsen T.

Journal of Machine Learning Research - 1/4/2004

10.1162/153244304773633852

Cite count: 16 (Web of Science) 32 (Scopus)
  • SJR Quartile: Q1
  • CiteScore: 6.8 (2020)
  • SJR: 1.691 (2004
  • SNIP: 6.419 (2004
  • SJR Categories: Artificial Intelligence (Q1); Control and Systems Engineering (Q1); Software (Q1); Statistics and Probability (Q1)

Failure modeling and maintenance optimization for a railway line

  • Hokstad P.
  • Langseth H.
  • Lindqvist B.
  • Vatn J.

International Journal of Performability Engineering - 1/1/2005

Cite count: 8 (Scopus)
  • CiteScore: 0.8 (2020)
  • SJR Categories: Safety, Risk, Reliability and Quality

Latent classification models

  • Langseth H.
  • Nielsen T.

Machine Learning - 1/6/2005

10.1007/s10994-005-0472-5

Cite count: 10 (Web of Science) 13 (Scopus)
  • SJR Quartile: Q1
  • CiteScore: 6.1 (2020)
  • SJR: 2.604 (2005
  • SNIP: 5.656 (2005
  • SJR Categories: Artificial Intelligence (Q1); Software (Q1)

Competing risks for repairable systems: A data study

  • Langseth H.
  • Lindqvist B.

Journal of Statistical Planning and Inference - 1/5/2006

10.1016/j.jspi.2004.10.032

Cite count: 33 (Scopus)
  • SJR Quartile: Q2
  • CiteScore: 1.4 (2020)
  • SJR: 0.937 (2006
  • SNIP: 1.051 (2006
  • SJR Categories: Applied Mathematics (Q2); Statistics and Probability (Q2); Statistics, Probability and Uncertainty (Q2)

Classification using Hierarchical Naïve Bayes models

  • Langseth H.
  • Nielsen T.

Machine Learning - 1/5/2006

10.1007/s10994-006-6136-2

Cite count: 47 (Web of Science) 62 (Scopus)
  • SJR Quartile: Q1
  • CiteScore: 6.1 (2020)
  • SJR: 1.8 (2006
  • SNIP: 4.144 (2006
  • SJR Categories: Artificial Intelligence (Q1); Software (Q1)

Applications of Bayesian networks in reliability analysis

  • Langseth H.
  • Portinale L.

Bayesian Network Technologies: Applications and Graphical Models - 1/12/2007

10.4018/978-1-59904-141-4.ch005

Cite count: 7 (Scopus)

Bayesian networks in reliability: The good, the bad, and the ugly

  • Langseth H.

Advances in Mathematical Modeling for Reliability - 1/5/2008

Cite count: 14 (Scopus)

Inference in hybrid Bayesian networks with Mixtures of Truncated Basis Functions

  • Langseth H.
  • Nielsen T.
  • Rumí R.
  • Salmerón A.

Proceedings of the Sixth European Workshop on Probabilistic Graphical Models - 1/12/2012

Cite count: 11 (Scopus)
  • SJR Categories: Statistics and Probability

Learning hybrid bayesian networks using mixtures of truncated basis functions. Aprendizaje de redes bayesianas híbridas con mixturas de funciones base truncadas

  • Inmaculada Pérez-Bernabé
  • Antonio Salmerón Cerdán
  • Helge Langseth

2015

Cite count:
  • Dialnet
Open Access

Parameter estimation in mixtures of truncated exponentials

  • Langseth H.
  • Nielsen T.
  • Rumí R.
  • Salmerón A.

Proceedings of the Fourth European Workshop on Probabilistic Graphical Models - 1/12/2008

Cite count: 3 (Scopus)
  • SJR Categories: Statistics and Probability

Local-global-learning of naive Bayesian classifier

  • Zhong S.
  • Langseth H.

2009 4th International Conference on Innovative Computing, Information and Control, ICICIC 2009 - 1/12/2009

10.1109/icicic.2009.254

Cite count: 2 (Scopus)
  • SJR Categories: Computational Theory and Mathematics; Computer Networks and Communications; Software
Open Access

Architectures integrating case-based reasoning and Bayesian networks for clinical decision support

  • Bruland T.
  • Aamodt A.
  • Langseth H.

IFIP Advances in Information and Communication Technology - 1/1/2010

10.1007/978-3-642-16327-2-13

Cite count: 17 (Scopus)
  • SJR Quartile: Q4
  • CiteScore: 1 (2020)
  • SJR: 0.142 (2010
  • SNIP: 0.273 (2010
  • SJR Categories: Computer Networks and Communications (Q4); Information Systems (Q4); Information Systems and Management (Q4)

Towards a more expressive model for dynamic classification

  • Zhong S.
  • Martínez A.
  • Nielsen T.
  • Langseth H.

Proceedings of the 23rd International Florida Artificial Intelligence Research Society Conference, FLAIRS-23 - 19/10/2010

Cite count: 3 (Scopus)
  • SJR Categories: Artificial Intelligence; Control and Systems Engineering
Open Access

Parameter learning in MTE networks using incomplete data

  • Fernández A.
  • Langseth H.
  • Nielsen T.
  • Salmerón A.

The Fifth European Workshop on Probabilistic Graphical Models, Helsinki, Finland, 12/09/2010 - 1/12/2010

Cite count: 4 (Scopus)
  • SJR Categories: Statistics and Probability

Extended abstract: Combining CBR and BN using metareasoning

  • Houeland T.
  • Bruland T.
  • Aamodt A.
  • Langseth H.

Frontiers in Artificial Intelligence and Applications - 1/1/2011

10.3233/978-1-60750-754-3-189

Cite count: 1 (Scopus)
  • SJR Quartile: Q3
  • CiteScore: 0.6 (2020)
  • SJR: 0.246 (2011
  • SNIP: 0.583 (2011
  • SJR Categories: Artificial Intelligence (Q3)

Extended abstract: A design for a tourist CF system

  • Lillegraven T.
  • Wolden A.
  • Kofod-Petersen A.
  • Langseth H.

Frontiers in Artificial Intelligence and Applications - 1/1/2011

10.3233/978-1-60750-754-3-193

Cite count:
  • SJR Quartile: Q3
  • CiteScore: 0.6 (2020)
  • SJR: 0.246 (2011
  • SNIP: 0.583 (2011
  • SJR Categories: Artificial Intelligence (Q3)

A hybrid CBR and BN architecture refined through data analysis

  • Bruland T.
  • Aamodt A.
  • Langseth H.

International Conference on Intelligent Systems Design and Applications, ISDA - 1/12/2011

10.1109/isda.2011.6121773

Cite count: 4 (Scopus)
  • SJR Categories: Artificial Intelligence; Computer Science Applications; Control and Systems Engineering; Signal Processing
Open Access

Mixtures of truncated basis functions

  • Langseth H.
  • Nielsen T.
  • Rumí R.
  • Salmerón A.

INTERNATIONAL JOURNAL OF APPROXIMATE REASONING - 1/2/2012

10.1016/j.ijar.2011.10.004

Cite count: 56 (Web of Science) 55 (Scopus)
  • JCR Quartile: Q2 (2012)
  • SJR Quartile: Q1
  • JCR Impact Factor: 1.729 (2012)
  • Category normalized Impact: 1.763 (2012)
  • CiteScore: 7.1 (2020)
  • SJR: 1.494 (2012
  • SNIP: 2.132 (2012
  • JCR 5-year Impact Factor: 2.165
  • JCR Categories: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
  • SJR Categories: Applied Mathematics (Q1); Artificial Intelligence (Q1); Software (Q1); Theoretical Computer Science (Q1)
Open Access

Fast approximate inference in hybrid Bayesian networks using dynamic discretisation

  • Langseth H.
  • Marquez D.
  • Neil M.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) - 23/10/2013

10.1007/978-3-642-38637-4_23

Cite count: 2 (Scopus)
  • CiteScore: 1.8 (2020)
  • SJR: 0.36 (2013
  • SNIP: 0.775 (2013

This author has no patents.

This author has no reports or other types of publications.

Scopus: 18

Web of Science: 12

Scopus: 33

Web of Science: 13

Last data update: 5/18/24 9:32 AM
Next scheduled update: 5/25/24 3:00 AM