RDF/XML JSON-LD N3/Turtle RDF/JSON

Langseth, Helge Autor

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

Número de citas: 17 (Web of Science) 16 (Scopus)
  • Cuartil JCR: Q3 (2018)
  • Cuartil SJR: Q1
  • Factor Impacto JCR: 1,82 (2022)
  • Impacto Normalizado por Categoría: 0,256 (2018)
  • CiteScore: 3,23 (2018)
  • SJR: 0,754 (2018)
  • SNIP: 2,084 (2018)
  • Impacto JCR a 5 años: 2,448
  • Categorías JCR: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
  • Categorías SJR: Artificial Intelligence (Q1)

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

Número de citas:
  • CiteScore: 7,6 (2022)
  • SJR: 1,228 (2022)
  • SNIP: 2,239 (2022)

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

Número de citas: 4 (Web of Science) 3 (Scopus)
  • Cuartil SJR: Q1
  • Factor Impacto JCR: 2,753 (2019)
  • CiteScore: 4,5 (2020)
  • SJR: 1,288 (2019)
  • SNIP: 2,113 (2019)
  • Impacto JCR a 5 años: 2,935
  • Categorías JCR: ENGINEERING, OCEAN
  • Categorías SJR: Ocean Engineering (Q1)

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

Número de citas: 62 (Web of Science) 50 (Scopus)
  • Cuartil JCR: Q1 (2017)
  • Cuartil SJR: Q1
  • Factor Impacto JCR: 4,396 (2022)
  • Impacto Normalizado por Categoría: 2,246 (2017)
  • CiteScore: 11,3 (2020)
  • SJR: 1,378 (2017)
  • SNIP: 2,349 (2017)
  • Impacto JCR a 5 años: 4,514
  • Categorías JCR: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
  • Categorías SJR: Artificial Intelligence (Q1); Information Systems and Management (Q1); Management Information Systems (Q1); Software (Q1)

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

Número de citas: 7 (Web of Science) 1 (Scopus)
  • Cuartil JCR: Q4 (2020)
  • Factor Impacto JCR: 0,86 (2022)
  • Impacto Normalizado por Categoría: 0,379 (2020)
  • CiteScore: 1,7 (2020)
  • SJR: 0,231 (2020)
  • SNIP: 0,527 (2020)
  • Impacto JCR a 5 años: 0,894
  • Categorías JCR: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

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

Número de citas: 18 (Scopus)
  • CiteScore: 9,3 (2020)

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.
... Ver más Contraer

Knowledge-Based Systems - 1/01/2019

10.1016/j.knosys.2018.09.019

Número de citas: 7 (Web of Science) 7 (Scopus)
  • Cuartil JCR: Q1 (2019)
  • Cuartil SJR: Q1
  • Factor Impacto JCR: 5,921 (2022)
  • Impacto Normalizado por Categoría: 0,374 (2019)
  • CiteScore: 11,3 (2020)
  • SJR: 1,754 (2019)
  • SNIP: 2,828 (2019)
  • Impacto JCR a 5 años: 6,075
  • Categorías JCR: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
  • Categorías SJR: Artificial Intelligence (Q1); Information Systems and Management (Q1); Management Information Systems (Q1); Software (Q1)
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

Número de citas: 23 (Web of Science) 31 (Scopus)
  • Cuartil SJR: Q1
  • Factor Impacto JCR: 1,729 (2012)
  • CiteScore: 7,1 (2020)
  • SJR: 1,494 (2012)
  • SNIP: 2,132 (2012)
  • Impacto JCR a 5 años: 2,165
  • Categorías JCR: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
  • Categorías SJR: Applied Mathematics (Q1); Artificial Intelligence (Q1); Software (Q1); Theoretical Computer Science (Q1)

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

Número de citas: 55 (Scopus)
  • Cuartil SJR: Q1
  • Factor Impacto JCR: 3,892 (2018)
  • CiteScore: 8,6 (2020)
  • SJR: 1,043 (2018)
  • SNIP: 3,011 (2018)
  • Impacto JCR a 5 años: 3,295
  • Categorías JCR: INFORMATION SCIENCE & LIBRARY SCIENCE
  • Categorías SJR: Computer Science Applications (Q1); Information Systems (Q1); Library and Information Sciences (Q1); Management Science and Operations Research (Q1); Media Technology (Q1)

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

Número de citas: 5 (Web of Science) 4 (Scopus)
  • Cuartil SJR: Q1
  • Factor Impacto JCR: 2,41 (2014)
  • CiteScore: 9,3 (2020)
  • SJR: 1,419 (2014)
  • SNIP: 2,616 (2014)
  • Impacto JCR a 5 años: 2,693
  • Categorías JCR: OPERATIONS RESEARCH & MANAGEMENT SCIENCE
  • Categorías SJR: Applied Mathematics (Q1); Industrial and Manufacturing Engineering (Q1); Safety, Risk, Reliability and Quality (Q1)

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

Número de citas:
  • Dialnet

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

Número de citas: 11 (Scopus)
  • Categorías SJR: Statistics and Probability

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

  • Langseth H.

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

Número de citas: 14 (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

Número de citas: 7 (Scopus)

Understanding and improving recurrent networks for human activity recognition by continuous attention

  • Zeng M.
  • Gao H.
  • Yu T.
  • Mengshoel O.
  • Langseth H.
  • Lane I.
  • Liu X.
... Ver más Contraer

Proceedings - International Symposium on Wearable Computers, ISWC - 8/10/2018

10.1145/3267242.3267286

Número de citas: 54 (Scopus)
  • Categorías SJR: Computer Networks and Communications; Engineering (miscellaneous); Hardware and Architecture; Software

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

Número de citas: 3 (Scopus)
  • Categorías SJR: Artificial Intelligence; Control and Systems Engineering
Open Access

Scalable MAP inference in Bayesian networks based on a Map-Reduce approach

  • Ramos-L pez D.o.
  • Salmer n A.
  • Rum R.
  • Mart nez A.M.
  • Nielsen T.D.
  • Masegosa A.R.
  • Langseth H.
  • Madsen A.L.
... Ver más Contraer

Proceedings of the Eighth International Conference on Probabilistic Graphical Models - 1/1/2016

Número de citas:
  • Factor Impacto JCR: 5 (2016)
  • CiteScore: 6,8 (2020)
  • SJR: 1,29 (2016)
  • SNIP: 2,222 (2016)
  • Impacto JCR a 5 años: 7,649
  • Categorías JCR: AUTOMATION & CONTROL SYSTEMS

Providing Previously Unseen Users Fair Recommendations Using Variational Autoencoders

  • Bjørnar Vassøy
  • Helge Langseth
  • Benjamin Kille

Proceedings of the 17th ACM Conference on Recommender Systems, RecSys 2023 - 14/09/2023

10.1145/3604915.3608842

Número de citas:

Probability-based approach for predicting e-commerce consumer behaviour using sparse session data

  • Myklatun Ø.
  • Thorrud T.
  • Nguyen H.
  • Langseth H.
  • Kofod-Petersen A.

Proceedings of the International ACM Recommender Systems Challenge 2015 - 16/9/2015

10.1145/2813448.2813514

Número de citas:
  • Categorías SJR: Engineering (miscellaneous)

Prediction intervals: Split normal mixture from quality-driven deep ensembles

  • Salem T.S.
  • Langseth H.
  • Ramampiaro H.

Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence, UAI 2020 - 1/1/2020

Número de citas: 1 (Scopus)
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

Número de citas: 4 (Scopus)
  • Categorías SJR: Statistics and Probability
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

Número de citas: 3 (Scopus)
  • Categorías SJR: Statistics and Probability
Open Access

Parallelisation of the PC Algorithm

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

Advances in Artificial Intelligence - 1/1/2015

10.1007/978-3-319-24598-0_2

Número de citas: 5 (Web of Science) 6 (Scopus)
  • Impacto Normalizado por Categoría: 0,64 (2015)
  • CiteScore: 1,8 (2020)
  • SJR: 0,369 (2015)
  • SNIP: 0,708 (2015)
Open Access

Parallel importance sampling in conditional linear Gaussian networks

  • Salmerón A.
  • Ramos-López D.
  • Borchani H.
  • Martínez A.
  • Masegosa A.
  • Fernández A.
  • Langseth H.
  • Madsen A.
  • Nielsen T.
... Ver más Contraer

Advances in Artificial Intelligence - 1/1/2015

10.1007/978-3-319-24598-0_4

Número de citas: 6 (Web of Science) 7 (Scopus)
  • Impacto Normalizado por Categoría: 0,767 (2015)
  • CiteScore: 1,8 (2020)
  • SJR: 0,369 (2015)
  • SNIP: 0,708 (2015)

Este autor no tiene patentes.

Este autor no tiene informes ni otros tipos de publicaciones.

Scopus: 18

Web of Science: 12

Scopus: 33

Web of Science: 13

Última actualización de los datos: 18/05/24 9:32
Próxima recolección programada: 25/05/24 3:00