Langseth, Helge Author

A classification-based approach to monitoring the safety of dynamic systems

  • Zhong, Shengtong
  • Langseth, Helge
  • Nielsen, Thomas Dyhre

RELIABILITY ENGINEERING and SYSTEM SAFETY - 1/01/2014

10.1016/j.ress.2013.07.016

Cite count: 5 (Web of Science) 5 (Scopus)

A deep network model for paraphrase detection in short text messages

  • Agarwal B.
  • Ramampiaro H.
  • Langseth H.
  • Ruocco M.

Information Processing and Management - 1/11/2018

10.1016/j.ipm.2018.06.005

Cite count: 102 (Scopus)
Open Access

A latent model for collaborative filtering

  • Langseth H.
  • Nielsen T.

INTERNATIONAL JOURNAL OF APPROXIMATE REASONING - 1/6/2012

10.1016/j.ijar.2011.11.002

Cite count: 23 (Web of Science) 38 (Scopus)
Open Access

AMIDST: A Java toolbox for scalable probabilistic machine learning

  • Masegosa A.
  • Martínez A.
  • Ramos-López D.
  • Cabañas R.
  • Salmerón A.
  • Langseth H.
  • Nielsen T.
  • Madsen A.
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KNOWLEDGE-BASED SYSTEMS - 1/1/2019

10.1016/j.knosys.2018.09.019

Cite count: 9 (Web of Science) 10 (Scopus)

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: 21 (Scopus)

Analyzing concept drift: A case study in the financial sector

  • Masegosa A.R.
  • Martínez A.M.
  • Ramos-López D.
  • Langseth H.
  • Nielsen T.D.
  • Salmerón A.

INTELLIGENT DATA ANALYSIS - 21/05/2020

10.3233/ida-194515

Cite count: 11 (Web of Science) 14 (Scopus)

A parallel algorithm for Bayesian network structure learning from large data sets

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

KNOWLEDGE-BASED SYSTEMS - 1/2/2017

10.1016/j.knosys.2016.07.031

Cite count: 71 (Web of Science) 80 (Scopus)
Open Access

Application of data-driven models in the analysis of marine power systems

  • Swider A.
  • Langseth H.
  • Pedersen E.

APPLIED OCEAN RESEARCH - 1/11/2019

10.1016/j.apor.2019.101934

Cite count: 6 (Web of Science) 7 (Scopus)

Applying Bayesian optimization to predict parameters in a time-domain model for cross-flow vortex-induced vibrations

  • Andersen M.L.
  • Sævik S.
  • Wu J.
  • Leira B.J.
  • Langseth H.

Marine Structures - 1/3/2024

10.1016/j.marstruc.2023.103571

Cite count: 3 (Scopus)
Open Access

A Review of Inference Algorithms for Hybrid Bayesian Networks

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

JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH - 1/8/2018

10.1613/jair.1.11228

Cite count: 31 (Web of Science) 46 (Scopus)

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: 8 (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)

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: 0 (Dialnet)
  • Dialnet

New Ideas in Ranking for Personalized Fashion Recommender Systems

  • Ramampiaro H.
  • Langseth H.
  • Almenningen T.
  • Schistad H.
  • Havig M.
  • Nguyen H.T.

Business and Consumer Analytics New Ideas - 1/1/2019

10.1007/978-3-030-06222-4_25

Cite count: 2 (Scopus)

STATISTICAL MODELING AND INFERENCE FOR COMPONENT FAILURE TIMES UNDER PREVENTIVE MAINTENANCE AND INDEPENDENT CENSORING

  • Lindqvist B.H.
  • Langseth H.

Series on Quality Reliability and Engineering Statistics Vol 10 Modern Statistical and Mathematical Methods in Reliability - 1/1/2005

10.1142/9789812703378_0023

Cite count: 3 (Scopus)
Open Access

A Divide and Conquer Approach for Solving Structural Causal Models

  • Bjøru, Anna Rodum
  • Cabañas De Paz, Rafael
  • Langseth, Helge
  • Salmerón Cerdán, Antonio

INTERNATIONAL CONFERENCE ON PROBABILISTIC GRAPHICAL MODELS - 1/1/2024

Cite count: 1 (Web of Science) 1 (Scopus)

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: 5 (Scopus)
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.

PROBABILISTIC GRAPHICAL MODELS - 1/1/2014

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

Cite count: 10 (Web of Science) 11 (Scopus)
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: 21 (Scopus)

A Reparameterization of Mixtures of Truncated Basis Functions and its Applications

  • Antonio Salmerón
  • Helge Langseth
  • Andres Masegosa
  • Thomas Dyhre Nielsen

Proceedings of Machine Learning Research - 1/1/2022

Cite count: 0 (Scopus)

ASAP: Attention-Based State Space Abstraction for Policy Summarization

  • Bekkemoen Y.
  • Langseth H.

Proceedings of Machine Learning Research - 1/1/2023

Cite count: 1 (Scopus)
Open Access

Bayesian Exploration in Deep Reinforcement Learning

  • Killingberg L.
  • Langseth H.

Ceur Workshop Proceedings - 1/1/2023

Cite count: 0 (Scopus)

Bayesian models of data streams with hierarchical power priors

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

34th International Conference on Machine Learning Icml 2017 - 1/1/2017

Cite count: 6 (Scopus)

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

  • Langseth H.

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

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

Cite count: 7 (Scopus)

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: 5 (Scopus)

This author has no patents.

This author has no reports or other types of publications.

Scopus: 21

Web of Science: 12

Scopus: 44

Web of Science: 19

Last data update: 6/7/25 1:31 PM
Next scheduled update: 6/14/25 3:00 AM