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Langseth, Helge Author

Inference in hybrid Bayesian networks

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

RELIABILITY ENGINEERING and SYSTEM SAFETY - 1/10/2009

10.1016/j.ress.2009.02.027

Cite count: 71 (Web of Science) 75 (Scopus)
  • JCR Quartile: Q1 (2009)
  • SJR Quartile: Q1
  • JCR Impact Factor: 1.908 (2009)
  • Category normalized Impact: 2.242 (2009)
  • CiteScore: 9.3 (2020)
  • SJR: 1.142 (2009
  • SNIP: 2.571 (2009
  • JCR 5-year Impact Factor: 2.305
  • JCR Categories: ENGINEERING, INDUSTRIAL
  • SJR Categories: Applied Mathematics (Q1); Industrial and Manufacturing Engineering (Q1); Safety, Risk, Reliability and Quality (Q1)

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)

Latent Classification Models for Binary Data

  • Langseth H.
  • Nielsen T.

Pattern Recognition - 1/11/2009

10.1016/j.patcog.2009.05.002

Cite count: 8 (Web of Science) 8 (Scopus)
  • SJR Quartile: Q1
  • JCR Impact Factor: 2.554 (2009)
  • CiteScore: 15.7 (2020)
  • SJR: 1.163 (2009
  • SNIP: 2.837 (2009
  • JCR 5-year Impact Factor: 3.453
  • JCR Categories: ENGINEERING, ELECTRICAL & ELECTRONIC
  • SJR Categories: Artificial Intelligence (Q1); Computer Vision and Pattern Recognition (Q1); Signal Processing (Q1); Software (Q1)
Open Access

Learning Mixtures of Truncated Basis Functions from Data

  • Langseth H.
  • Nielsen T.
  • Pérez-Bernabé I.
  • Salmerón A.

International Journal of Approximate Reasoning - 1/1/2014

10.1016/j.ijar.2013.09.012

Cite count: 23 (Web of Science) 9 (Scopus)
  • JCR Quartile: Q1 (2014)
  • SJR Quartile: Q1
  • JCR Impact Factor: 2.451 (2022)
  • Category normalized Impact: 0.81 (2014)
  • CiteScore: 7.1 (2020)
  • SJR: 1.46 (2014
  • SNIP: 2.845 (2014
  • JCR 5-year Impact Factor: 2.51
  • JCR Categories: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
  • SJR Categories: Applied Mathematics (Q1); Artificial Intelligence (Q1); Software (Q1); Theoretical Computer Science (Q1)

Learning similarity measures from data

  • Mathisen B.
  • Aamodt A.
  • Bach K.
  • Langseth H.

Progress in Artificial Intelligence - 1/1/2019

10.1007/s13748-019-00201-2

Cite count: 21 (Web of Science) 15 (Scopus)
  • SJR Quartile: Q2
  • CiteScore: 3.4 (2020)
  • SJR: 0.322 (2020
  • SNIP: 0.746 (2020
  • SJR Categories: Artificial Intelligence (Q2)

Machine Learning in Financial Market Surveillance: A Survey

  • Tiwari S.
  • Ramampiaro H.
  • Langseth H.

IEEE Access - 1/1/2021

10.1109/access.2021.3130843

Cite count:
  • JCR Impact Factor: 3.476 (2021)
  • JCR 5-year Impact Factor: 3.758
  • JCR Categories: COMPUTER SCIENCE, INFORMATION SYSTEMS
Open Access

MAP inference in dynamic hybrid Bayesian networks

  • Ramos-López D.
  • Masegosa A.
  • Martínez A.
  • Salmerón A.
  • Nielsen T.
  • Langseth H.
  • Madsen A.
... View more Collapse

Progress in Artificial Intelligence - 27/06/2017

10.1007/s13748-017-0115-7

Cite count: 6 (Web of Science) 4 (Scopus)
  • Category normalized Impact: 0.228 (2017)
  • CiteScore: 3.4 (2020)
  • SNIP: 0.943 (2017
  • SJR Categories: Artificial Intelligence
Open Access

Maximum Likelihood Learning of Conditional MTE Distributions

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

Lecture Notes in Computer Science - 27/8/2009

10.1007/978-3-642-02906-6_22

Cite count: 9 (Web of Science) 14 (Scopus)
  • JCR Quartile: Q2 (2009)
  • Category normalized Impact: 1.409 (2009)
  • CiteScore: 1.8 (2020)
  • SJR: 0.302 (2009
  • SNIP: 0.586 (2009

Modelling of dependence between critical failure and preventive maintenance: The repair alert model

  • Lindqvist B.
  • Støve B.
  • Langseth H.

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

10.1016/j.jspi.2004.10.033

Cite count: 22 (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)
Open Access

Parameter Estimation and Model Selection for Mixtures of Truncated Exponentials

  • Antonio Salmerón Cerdán
  • Rafael Rumí Rodríguez
  • Thomas D. Nielsen
  • Helge Langseth

International Journal of Approximate Reasoning - 1/06/2010

10.1016/j.ijar.2010.01.008

Cite count: 22 (Web of Science) 27 (Scopus)
  • JCR Quartile: Q2 (2010)
  • SJR Quartile: Q1
  • JCR Impact Factor: 1.684 (2010)
  • Category normalized Impact: 0.66 (2010)
  • CiteScore: 7.1 (2020)
  • SJR: 1.329 (2010
  • SNIP: 1.971 (2010
  • JCR 5-year Impact Factor: 1.72
  • JCR Categories: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
  • SJR Categories: Applied Mathematics (Q1); Artificial Intelligence (Q1); Software (Q1); Theoretical Computer Science (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

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

A new method for vertical parallelisation of TAN learning based on balanced incomplete block designs

  • Madsen A.
  • Jensen F.
  • Salmerón A.
  • Karlsen M.
  • Langseth H.
  • Nielsen T.

Proceedings of the 7th European Workshop on Probabilistic Graphical Models - 1/1/2014

10.1007/978-3-319-11433-0_20

Cite count: 9 (Web of Science) 10 (Scopus)
  • Category normalized Impact: 0.839 (2014)
  • CiteScore: 1.8 (2020)
  • SJR: 0.354 (2014
  • SNIP: 0.753 (2014
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)
Open Access

Bayesian Exploration in Deep Reinforcement Learning

  • Killingberg L.
  • Langseth H.

CEUR Workshop Proceedings - 1/1/2023

Cite count:
  • CiteScore: 1.1 (2022)
  • SJR: 0.202 (2022
  • SNIP: 0.223 (2022

Bayesian models of data streams with Hierarchical Power Priors

  • Masegosa A.
  • Nielsen T.
  • Langseth H.
  • Ramos-López D.
  • Salmerón A.
  • Madsen A.

Proceedings of the 34th International Conference on Machine Learning - 1/1/2017

Cite count: 2 (Scopus)

Beating the bookie: A look at statistical models for prediction of football matches

  • Langseth H.

Frontiers in Artificial Intelligence and Applications - 1/12/2013

10.3233/978-1-61499-330-8-165

Cite count: 5 (Scopus)
  • SJR Quartile: Q3
  • CiteScore: 0.6 (2020)
  • SJR: 0.275 (2013
  • SNIP: 0.521 (2013
  • SJR Categories: Artificial Intelligence (Q3)

Comparative study of event prediction in power grids using supervised machine learning methods

  • Hoiem K.W.
  • Santi V.
  • Torsater B.N.
  • Langseth H.
  • Andresen C.A.
  • Rosenlund G.H.

SEST 2020 - 3rd International Conference on Smart Energy Systems and Technologies - 1/9/2020

10.1109/sest48500.2020.9203025

Cite count: 2 (Scopus)
Open Access

Content-Based Social Recommendation with Poisson Matrix Factorization

  • da Silva E.
  • Langseth H.
  • Ramampiaro H.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) - 1/1/2017

10.1007/978-3-319-71249-9_32

Cite count: 9 (Scopus)
  • CiteScore: 1.8 (2020)
  • SJR: 0.295 (2017
  • SNIP: 0.673 (2017
Open Access

Data driven case base construction for prediction of success of marine operations

  • Mathisen B.
  • Aamodt A.
  • Langseth H.

CEUR Workshop Proceedings - 1/1/2017

Cite count: 1 (Scopus)
  • CiteScore: 0.8 (2020)
  • SJR: 0.167 (2017
  • SNIP: 0.346 (2017
  • SJR Categories: Computer Science (miscellaneous)

Detection of Potential Manipulations in Electricity Market using Machine Learning Approaches

  • Tiwari, Shweta
  • Bell, Gavin
  • Langseth, Helge
  • Ramampiaro, Heri
  • Rocha, AP
  • Steels, L
  • VandenHerik, J
... View more Collapse

ICAART: PROCEEDINGS OF THE 14TH INTERNATIONAL CONFERENCE ON AGENTS AND ARTIFICIAL INTELLIGENCE - VOL 3 - 2022

10.5220/0010991800003116

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