H. Langseth

发表

Bayesian Networks in Reliability

H. Langseth, L. Portinale, 2022 .

Parameter Estimation in Mixtures of Truncated Exponentials

Thomas D. Nielsen, A. Salmerón, Rafael Rumí, 2008 .

eqr 439 : Bayesian belief network computation for reliability

F. V. Jensen, H. Langseth, 2006 .

Aalborg Universitet Modeling concept drift A probabilistic graphical model based approach

Ana M. Martínez, Thomas D. Nielsen, A. Madsen, 2015 .

An evolutionary mixture-of-experts approach to data cleansing and load prediction

Pınar Öztürk, H. Langseth, A. Tidemann, 2013 .

Aalborg Universitet MAP inference in dynamic hybrid Bayesian networks

Ana M. Martínez, Thomas D. Nielsen, A. Madsen, 2017 .

A hybrid metareasoning architecture combining case-based reasoning and Bayesian networks

A. Aamodt, H. Langseth, T. Houeland, 2011 .

Tourist Without a Cause

A. Kofod-Petersen, H. Langseth, Anders Kofod-Petersen, 2010 .

Aalborg Universitet A latent model for collaborative filtering

Thomas Dyhre, H. Langseth, 2009 .

Aalborg Universitet Inference in hybrid Bayesian networks

Thomas D. Nielsen, A. Salmerón, Rafael Rumí, 2008 .

Explanations in Bayesian Networks using Provenance through Case-based Reasoning

A. Aamodt, A. Kofod-Petersen, H. Langseth, 2010 .

Applications of Bayesian Networks in Reliability Analysis

H. Langseth, L. Portinale, 2007 .

Bayesian Networks in Reliability Analysis

Helge Langseth, H. Langseth, 2007 .

Aalborg Universitet Parameter estimation in mixtures of truncated exponentials

Thomas Dyhre, H. Langseth, 2008 .

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

Helge Langseth, Bo Henry Lindqvist, Bård Støve, 2006 .

Bayesian Networks in Reliability : The Good , the Bad , and the Ugly

H. Langseth, 2007 .

Aalborg Universitet Parallelisation of the PC Algorithm

Thomas D. Nielsen, A. Madsen, Frank Jensen, 2017 .

A data-driven modular architecture with denoising autoencoders for health indicator construction in a manufacturing process

Emil Blixt Hansen, N. Iftikhar, H. Langseth, 2022, ArXiv.

Heuristics for Two Extensions of Basic Troubleshooting

Helge Langseth, Finn Verner Jensen, F. V. Jensen, 2001, SCAI.

Classification using Hierachical Na¨õve Bayes models

Thomas D. Nielsen, Helge Langseth, H. Langseth, 2004 .

Aalborg Universitet Maximum Likelihood Learning of Conditional MTE Distributions

Thomas D. Nielsen, A. Salmerón, Rafael Rumí, 2009 .

Project full title: Analysis of Massive Data STreams

Rafael Rumí, Thomas D. Nielsen, Helge Langseth, 2014 .

Uncertainty Bounds for a Monotone Multistate System

Helge Langseth, Bo Henry Lindqvist, B. Lindqvist, 1998, Probability in the Engineering and Informational Sciences.

A Reparameterization of Mixtures of Truncated Basis Functions and its Applications

Thomas D. Nielsen, A. Salmerón, H. Langseth, 2022, PGM.

A latent model for collaborative filtering

Thomas D. Nielsen, Helge Langseth, H. Langseth, 2012, Int. J. Approx. Reason..

Scalable learning of probabilistic latent models for collaborative filtering

Thomas D. Nielsen, Helge Langseth, H. Langseth, 2015, Decis. Support Syst..

Learning Retrieval Knowledge from Data

Agnar Aamodt, Helge Langseth, Ole Martin Winnem, 1999 .

Bayesian networks with applications in reliability analysis

Helge Langseth, H. Langseth, 2002 .

Modelling Maintenance for Components Under Competing Risk

Helge Langseth, H. Langseth, 1999 .

Modelling maintenance for components under competing riskHelge

H. Langseth, 1999 .

Scaling up Bayesian variational inference using distributed computing clusters

Andrés R. Masegosa, Anders L. Madsen, Antonio Salmerón, 2017, Int. J. Approx. Reason..

Integrating Bayesian Networks into Knowledge-Intensive CBR

Agnar Aamodt, Helge Langseth, A. Aamodt, 1998 .

Probabilistic Models with Deep Neural Networks

Andrés R. Masegosa, Antonio Salmerón, Rafael Cabañas, 2019, Entropy.

Learning similarity measures from data

Agnar Aamodt, Kerstin Bach, Bjørn Magnus Mathisen, 2019, Progress in Artificial Intelligence.

Analyzing concept drift: A case study in the financial sector

Andrés R. Masegosa, Antonio Salmerón, Ana M. Martínez, 2020, Intell. Data Anal..

Fusion of Domain Knowledge with Data for Structural Learning in Object Oriented Domains

Thomas D. Nielsen, Helge Langseth, H. Langseth, 2003, J. Mach. Learn. Res..

Bayesian Networks in Reliability: Some Recent Developments

Helge Langseth, Finn Verner Jensen, H. Langseth, 2004 .

Prediction Intervals: Split Normal Mixture from Quality-Driven Deep Ensembles pdf

Heri Ramampiaro, Helge Langseth, Tárik S. Salem, 2020, UAI.

Effective hate-speech detection in Twitter data using recurrent neural networks

Heri Ramampiaro, Helge Langseth, Georgios K. Pitsilis, 2018, Applied Intelligence.

Aalborg Universitet Inference in hybrid Bayesian networks

Rafael Rumí, Antonio Salmerón, Thomas D. Nielsen, 2016 .

Decision theoretic troubleshooting of coherent systems

Helge Langseth, Finn Verner Jensen, F. V. Jensen, 2003, Reliab. Eng. Syst. Saf..

The SACSO methodology for troubleshooting complex systems

F. V. Jensen, Uffe Kjærulff, Claus Skaanning, 2001, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

MAP inference in dynamic hybrid Bayesian networks

Andrés R. Masegosa, Anders L. Madsen, Ana M. Martínez, 2017, Progress in Artificial Intelligence.

Effective hate-speech detection in Twitter data using recurrent neural networks pdf

Heri Ramampiaro, Helge Langseth, Georgios K. Pitsilis, 2018, Applied Intelligence.

Classification using Hierarchical Naïve Bayes models

Thomas D. Nielsen, Helge Langseth, H. Langseth, 2006, Machine Learning.

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

Ming Zeng, Ole J. Mengshoel, Tong Yu, 2018, UbiComp.

Bayesian Networks in Reliability : The Good , the Bad , and the Ugly

H. Langseth, 2008 .

Bayesian Networks in Reliability: Some Recent Developments

Helge Langseth, H. Langseth, 2004 .

Forecasting Intra-Hour Imbalances in Electric Power Systems

Heri Ramampiaro, Helge Langseth, Tárik S. Salem, 2019, AAAI.

Effective hate-speech detection in Twitter data using recurrent neural networks

Georgios K. Pitsilis, H. Ramampiaro, H. Langseth, 2018, Applied Intelligence.

Structural Learning in Object Oriented Domains

Thomas D. Nielsen, Helge Langseth, Olav Bangsø, 2001, FLAIRS.

Parameter Learning in Object-Oriented Bayesian Networks

Helge Langseth, Olav Bangsø, Olav Bangsø, 2001, Annals of Mathematics and Artificial Intelligence.

Content-Based Social Recommendation with Poisson Matrix Factorization

Heri Ramampiaro, Helge Langseth, Eliezer de Souza da Silva, 2017, ECML/PKDD.

A Review of Inference Algorithms for Hybrid Bayesian Networks

Rafael Rumí, Anders L. Madsen, Antonio Salmerón, 2018, J. Artif. Intell. Res..

Modeling Concept Drift: A Probabilistic Graphical Model Based Approach

Andrés R. Masegosa, Anders L. Madsen, Antonio Salmerón, 2015, IDA.

Variational Inference over Nonstationary Data Streams for Exponential Family Models

Andrés R. Masegosa, Antonio Salmerón, Darío Ramos-López, 2020, Mathematics.

Bayesian Models of Data Streams with Hierarchical Power Priors

Andrés R. Masegosa, Anders L. Madsen, Darío Ramos-López, 2017, ICML.

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

Anders L. Madsen, Antonio Salmerón, Thomas D. Nielsen, 2017, Knowl. Based Syst..

New Ideas in Ranking for Personalized Fashion Recommender Systems

Heri Ramampiaro, Helge Langseth, Hai Thanh Nguyen, 2019, Business and Consumer Analytics: New Ideas.

AMIDST: a Java Toolbox for Scalable Probabilistic Machine Learning

Anders L. Madsen, Thomas D. Nielsen, Helge Langseth, 2017, Knowl. Based Syst..

Financial Data Analysis with PGMs Using AMIDST

Andrés R. Masegosa, Anders L. Madsen, Rafael Cabañas, 2016, 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW).

Dynamic Bayesian modeling for risk prediction in credit operations

Andrés R. Masegosa, Anders L. Madsen, Antonio Salmerón, 2015, SCAI.

Latent Classification Models

Thomas D. Nielsen, Helge Langseth, H. Langseth, 2005, Machine Learning.

Learning similarity measures from data

A. Aamodt, Kerstin Bach, H. Langseth, 2019, Progress in Artificial Intelligence.

Competing risks for repairable systems : A data study

Helge Langseth, Bo Henry Lindqvist, B. Lindqvist, 2006 .

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

Helge Langseth, Bo Henry Lindqvist, B. Lindqvist, 2005 .

A MAINTENANCE MODEL FOR COMPONENTS EXPOSED TO SEVERAL FAILURE MECHANISMS AND IMPERFECT REPAIR

Helge Langseth, Bo Henry Lindqvist, B. Lindqvist, 2002 .

On Reporting Robust and Trustworthy Conclusions from Model Comparison Studies Involving Neural Networks and Randomness

Odd Erik Gundersen, H. Langseth, Saeid Shamsaliei, 2023, ACM-REP.

Short-Term Load Forecasting With Seasonal Decomposition Using Evolution for Parameter Tuning

Helge Langseth, Axel Tidemann, Pinar Öztürk, 2015, IEEE Transactions on Smart Grid.

A Deep Network Model for Paraphrase Detection in Short Text Messages pdf

Heri Ramampiaro, Helge Langseth, Basant Agarwal, 2017, Inf. Process. Manag..

Consumer-side Fairness in Recommender Systems: A Systematic Survey of Methods and Evaluation

H. Langseth, Bjørnar Vassøy, 2023, ArXiv.

Parallel Filter-Based Feature Selection Based on Balanced Incomplete Block Designs

Andrés R. Masegosa, Anders L. Madsen, Ana M. Martínez, 2016, ECAI.

Correcting Classification: A Bayesian Framework Using Explanation Feedback to Improve Classification Abilities

H. Langseth, Yanzhe Bekkemoen, 2021, ArXiv.

Inter-Session Modeling for Session-Based Recommendation

Helge Langseth, Massimiliano Ruocco, Ole Steinar Lillestøl Skrede, 2017, DLRS@RecSys.

Latent classification models for binary data

Thomas D. Nielsen, Helge Langseth, H. Langseth, 2009, Pattern Recognit..

Effects of data cleansing on load prediction algorithms

Helge Langseth, Axel Tidemann, Boye Annfelt Høverstad, 2013, 2013 IEEE Computational Intelligence Applications in Smart Grid (CIASG).

Deep Contextual Grid Triplet Network for Context-Aware Recommendation

H. Ramampiaro, H. Langseth, M. Ruocco, 2023, IEEE Access.

Detection of Potential Manipulations in Electricity Market using Machine Learning Approaches

H. Ramampiaro, H. Langseth, S. Tiwari, 2022, ICAART.

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

Eilif Pedersen, Helge Langseth, Anna Swider, 2019, Applied Ocean Research.

Comparative Study of Event Prediction in Power Grids using Supervised Machine Learning Methods

Kristian Wang Høiem, Helge Langseth, Bendik Nybakk Torsæter, 2020, 2020 International Conference on Smart Energy Systems and Technologies (SEST).

Latent classification models for binary data

Thomas D. Nielsen, H. Langseth, 2009, Pattern Recognit..

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

Andrés R. Masegosa, Anders L. Madsen, Ana M. Martínez, 2016, Probabilistic Graphical Models.

Parallelisation of the PC Algorithm

Anders L. Madsen, Antonio Salmerón, Thomas D. Nielsen, 2015, CAEPIA.

A New Method for Vertical Parallelisation of TAN Learning Based on Balanced Incomplete Block Designs

Anders L. Madsen, Antonio Salmerón, Thomas D. Nielsen, 2014, Probabilistic Graphical Models.

Fast Approximate Inference in Hybrid Bayesian Networks Using Dynamic Discretisation

Helge Langseth, Martin Neil, David Marquez, 2013, IWINAC.

Providing Previously Unseen Users Fair Recommendations Using Variational Autoencoders

B. Kille, H. Langseth, Bjørnar Vassøy, 2023, RecSys.

Learning to Rank for Personalised Fashion Recommender Systems via Implicit Feedback

Heri Ramampiaro, Anders Kofod-Petersen, Helge Langseth, 2014, MIKE.

Requirement Engineering for a Small Project with Pre-Specified Scope

Andrés R. Masegosa, Sigve Hovda, Anders L. Madsen, 2014, NIK.

Machine Learning in Financial Market Surveillance: A Survey

Heri Ramampiaro, Helge Langseth, Shweta Tiwari, 2021, IEEE Access.

A hybrid CBR and BN architecture refined through data analysis

Agnar Aamodt, Helge Langseth, Tore Bruland, 2011, 2011 11th International Conference on Intelligent Systems Design and Applications.

Architectures Integrating Case-Based Reasoning and Bayesian Networks for Clinical Decision Support

Agnar Aamodt, Helge Langseth, Tore Bruland, 2010, Intelligent Information Processing.

The SACSO methodology for troubleshooting complex systems

Jirí Vomlel, Claus Skaanning, Helge Langseth, 2001, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.