"Official" listings:
2021
Shweta Tiwari,
Heri Ramampiaro,
and Helge Langseth (2021):
Machine Learning in Financial Market Surveillance: A Survey
IEEE Access 9: 159734-159754, 2021.
(Open access)
Yanzhe Bekkemoen and
and Helge Langseth (2021):
Correcting Classification: A Bayesian Framework Using Explanation Feedback to Improve Classification Abilities
(arXiv version)
Andrés Masegosa,
Rafael Cabañas,
Helge Langseth,
Thomas D. Nielsen, and
Antonio Salmerón (2021):
Probabilistic Models with Deep Neural Networks
Entropy 23(1), Paper 117.
(Open access, arXiv version)
2020
Tarik Salem,
Helge Langseth and
Heri Ramampiaro (2020):
Prediction Intervals: Split Normal Mixture from Quality-Driven Deep Ensembles
Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI-20),
Paper 485.
(Open access)
Andrés Masegosa,
Darío Ramos-López,
Antonio Salmerón,
Helge Langseth, and Thomas D. Nielsen (2020):
Variational Inference over Nonstationary Data Streams for Exponential Family Models
Mathematics 8(11), Paper 1942.
(Open access)
Andrés Masegosa,
Ana M. Martínez,
Darío Ramos-López,
Helge Langseth,
Thomas D. Nielsen, and
Antonio Salmerón (2020):
Analyzing concept drift: A case study in the financial sector.
Intelligent Data Analysis 24: 665-688, 2020.
(Paper)
Kristian Høiem, Vemund Santi, Bendik Torsæter, Helge Langseth, Christian Andresen and Gjert Rosenlund:
Comparative Study of Event Prediction in Power Grids using Supervised Machine Learning Methods
Presented at the 2020 International Conference on Smart Energy Systems and Technologies.
(Paper)
2019
Bjørn Magnus Mathisen,
Agnar Aamodt,
Kerstin Bach,
and Helge Langseth (2019):
Learning similarity measures from data.
Progress in AI.
(Open access)
Tarik Salem,
Karan Kathuria,
Heri Ramampiaro and Helge Langseth (2019):
Forecasting Intra-Hour Imbalances in Electric Power Systems.
Proceedings of the AAAI 31st Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-19).
(Paper, arXiv version)
Anna Swider, Helge Langseth, Eilif Pedersen (2019):
Application of data-driven models in the analysis of marine power systems.
Applied Ocean Research, 92, Article 101934, 2019
(Paper)
Heri Ramampiaro, Helge Langseth, Martin Havig,
Herman Schistad, Thomas Almenningen and Hai Thanh Nguyen (2019):
New Ideas in ranking for Personalised Fashion Recommender Systems
In: Business and Consumer Analytics: New Directions, Vol. 1
(Pablo Moscato and Natalie Jane de Vries, eds.), Springer. Pages 933-961.
(Paper)
Andrés Masegosa,
Ana M. Martínez,
Darío Ramos-López,
Rafael Cabañas, Antonio Salmerón,
Helge Langseth, Thomas D. Nielsen, and Anders L. Madsen (2019):
AMIDST: A Java toolbox for scalable probabilistic machine learning.
Knowledge-Based Systems 163: 595-597.
(Paper)
2018
Georgios Pitsilis,
Heri Ramampiaro, and Helge Langseth, (2018):
Securing Tag-Based Recommender Systems Against Profile Injection Attacks: A comparative Study.
Proceedings of the 12th ACM Conference on Recommender Systems (RecSys 2018),
Late-Breaking Results track.
(Open access)
Ming Zeng, Haoxiang Gao, Tong Yu, Ole J. Mengshoel,
Helge Langseth, Ian Lane, Xiaobing Liu (2018):
Understanding and Improving Recurrent Networks for Human Activity Recognition by Continuous Attention.
Proceedings of the 2018 ACM International Symposium on Wearable Computers.
(arXiv version,
Paper)
Antonio Salmerón,
Rafael Rumí,
Helge Langseth, Thomas D. Nielsen, and Anders L. Madsen (2018):
A Review of Inference Algorithms for Hybrid Bayesian Networks.
The journal of artificial intelligence research 63: 799-828, 2018.
(Open access)
Basant Agarwal, Heri Ramampiaro,
Helge Langseth and Massimiliano Ruocco (2018):
A deep network model for paraphrase detection in short text messages.
Information Processing & Management 54(6): 922-937, 2018.
(Preprint,
Paper)
Georgios Pitsilis,
Heri Ramampiaro, and Helge Langseth (2018):
Effective hate-speech detection in Twitter data using recurrent neural networks.
Applied intelligence 48: 4730-4742, 2018.
(Preprint,
Paper)
Darío Ramos-López,
Andrés Masegosa,
Antonio Salmerón,
Rafael Rumí,
Helge Langseth,
Thomas D. Nielsen, and
Anders L. Madsen (2018):
Scalable importance sampling estimation of Gaussian mixture posteriors in Bayesian networks.
International Journal of Approximate Reasoning 100: 115-134, 2018.
(Paper)
2017
Eliezer de Souza da Silva,
Helge Langseth and Heri Ramampiaro (2017):
Content-Based Social Recommendation with Poisson Matrix Factorization
The European Conference on Machine Learning and
Principles and practice of Knowledge Discovery in Databases
(Preprint,
Paper)
Andrés Masegosa,
Antonio Salmerón,
Darío Ramos-López
Helge Langseth, and Thomas D. Nielsen (2017):
Bayesian models of data streams with Hierarchical Power Priors
The Thirty-fourth International Conference on Machine Learning (ICML'17)
Proceedings of Machine Learning Research 70: 2334-2343, 2017.
(Open access)
Massimiliano Ruocco, Ole Steinar Lillestøl Skrede, and Helge Langseth (2017):
Inter-Session Modeling for Session-Based Recommendation
The 2nd WS on Deep Learning for Recommender systems at RECSYS
(Open access)
Andrés Masegosa,
Ana M. Martínez, Helge Langseth, Thomas D. Nielsen,
Antonio Salmerón, and
Darío Ramos-López (2017):
Scaling up Bayesian variational inference using distributed computing clusters
International Journal of Approximate Reasoning 88: 435-451, 2017.
(Preprint,
Paper)
Anders L. Madsen, Frank Jensen,
Antonio Salmerón,
Helge Langseth, and Thomas D. Nielsen (2017):
A Parallel Algorithm for Bayesian Network Structure Learning from Large Data Sets.
Knowledge-Based Systems 117: 46-55, 2017.
(Open access)
Darío Ramos-López,
Andrés Masegosa,
Ana M. Martínez,
Antonio Salmerón,
Thomas D. Nielsen,
Helge Langseth, and Anders L. Madsen (2017):
MAP inference in dynamic hybrid Bayesian networks.
Progress in Artificial Intelligence 6: 133-144, 2017.
(Open access)
2016
Andrés Masegosa,
Ana M. Martínez, Helge Langseth,
Thomas D. Nielsen,
Antonio Salmerón,
Darío Ramos-López, and
Anders L. Madsen (2016):
d-VMP: Distributed Variational Message Passing.
International Conference on Probabilistic Graphical Models
Journal of Machine Learning Research: Workshop and Conference Proceedings 52: 321-332, 2016
(Open access)
Darío Ramos-López,
Antonio Salmerón,
Rafael Rumí,
Ana M. Martínez, Thomas D. Nielsen,
Andrés Masegosa,
Helge Langseth, and Anders L. Madsen (2016):
Scalable MAP inference in Bayesian networks based on a Map-Reduce approach.
International Conference on Probabilistic Graphical Models
Journal of Machine Learning Research: Workshop and Conference Proceedings 52: 415-425, 2016
(Open access)
Antonio Salmerón,
Anders L. Madsen, Frank Jensen, Helge Langseth,
Thomas D. Nielsen,
Darío Ramos-López,
Ana M. Martínez, and
Andrés Masegosa (2016):
Parallel Filter-Based Feature Selection Based on Balanced Incomplete Block Designs.
22nd European Conference on Artificial Intelligence
Frontiers in Artificial Intelligence and Applications, Volume 285, pp. 743-750. IOS Press.
(Open access)
2015
Hanen Borchani, Ana M. Martínez,
Andrés Masegosa,
Helge Langseth,
Thomas D. Nielsen,
Antonio Salmerón,
Antonio Fernández, Anders L. Madsen and Ramón Sáez (2015):
Dynamic Bayesian modeling for risk prediction in credit operations.
The 13th Scandinavian Conference on Artificial Intelligence
(Preprint,
Paper)
Anders L. Madsen, Frank Jensen,
Antonio Salmerón,
Helge Langseth, and Thomas D. Nielsen (2015):
Parallelization of the PC Algorithm.
The XVI Conference of the Spanish Association for Artificial Intelligence (CAEPIA'15)
(Preprint,
Paper)
Antonio Salmerón,
Darío Ramos-López, Hanen Borchani, Ana M. Martínez,
Andrés Masegosa,
Antonio Fernández, Helge Langseth, Anders L. Madsen, and
Thomas D. Nielsen (2015):
Parallel Importance Sampling in Conditional Linear Gaussian Networks.
The XVI Conference of the Spanish Association for Artificial Intelligence (CAEPIA'15)
(Preprint,
Paper)
Hanen Borchani, Ana M. Martínez,
Andrés Masegosa,
Helge Langseth, Thomas D. Nielsen,
Antonio Salmerón,
Antonio Fernández, Anders L. Madsen, Ramón Sáez (2015):
Modeling concept drift: A probabilistic graphical model based approach
The Fourteenth International Symposium on Intelligent Data Analysis (IDA'15)
(Preprint, Paper)
Øyvind Myklatun, Thorstein Thorrud, Hai Nguyen, Helge Langseth, and
Anders Kofod-Petersen (2015):
Probability-based Approach for Predicting E-commerce Consumer Behaviour Using Sparse Session Data.
ACM Recsys challenge workshop (Recsys'15)
(Preprint, Paper)
Boye Annfelt Høverstad, Axel Tidemann, Helge Langseth and Pinar Öztürk (2015):
Short-Term Load Forecasting With Seasonal Decomposition Using Evolution for Parameter Tuning.
IEEE Transactions on Smart Grid 6(4): 1904-1913, 2015.
(Preprint, Paper)
Helge Langseth and Thomas D. Nielsen (2015):
Scalable learning of probabilistic latent models for collaborative filtering.
Decision Support Systems 74: 1-11, 2015.
(Preprint, Paper)
Inmaculada Pérez-Bernabé,
Antonio Salmerón and Helge Langseth (2015):
Learning Conditional Distributions using Mixtures of Truncated Basis Functions
The 13th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQUARU'15)
Springer Lecture Notes in Artificial Intelligence Volume 9161/2015, pp. 397-406
(Preprint, Paper)
Antonio Salmerón, Rafael Rumí, Helge Langseth, Anders L. Madsen and
Thomas D. Nielsen (2015):
MPE inference in Conditional Linear Gaussian Networks
The 13th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQUARU'15)
(Preprint, Paper)
2014
Anders L. Madsen, Frank Jensen, Martin Karlsen, Antonio Salmerón, Helge Langseth, and Thomas D. Nielsen
(2014):
A New Method for Vertical Parallelisation of TAN Learning Based on Balanced Incomplete Block Designs
Presented at the Seventh European Workshop on Probabilistic Graphical Models (PGM 2014),
Lecture Notes in Computer Science Vol. 8754, pp 302-317.
(Paper)
Helge Langseth, Thomas D. Nielsen, Inmaculada Pérez-Bernabé, and
Antonio Salmerón (2014):
Learning mixtures of truncated basis functions from data
International Journal of Approximate Reasoning 55(4): 940-956, 2014.
(Preprint, Paper)
Hai Thanh Nguyen, Thomas Almenningen, Martin Havig, Herman Schistad, Anders Kofod-Petersen, Helge Langseth, and Heri Ramampiaro (2014):
Learning to Rank for Personalized Fashion Recommender Systems via Implicit Feedback.
Presented at the Second International Conference on Mining Intelligence and Knowledge Exploration (MIKE 2014),
Lecture Notes in Computer Science Vol. 8891, pp. 51-61.
(Paper,
Dataset)
Shengtong Zhong, Helge Langseth and Thomas D. Nielsen (2014):
A classification-based approach to monitoring the safety of dynamic systems
Reliability Engineering and System Safety 121: 61-71, 2014.
(Preprint, Paper)
2013
Helge Langseth (2013):
Beating the bookie: A look at statistical models for prediction of football matches
Presented at the 12th Scandinavian AI conference, Aalborg, Denmark, November 20-22, 2013.
(Paper, Slides)
Helge Langseth, David Marquez, and Martin Neil (2013):
Fast approximate inference in hybrid Bayesian networks using dynamic discretisation
Presented at the Fifth International Work-Conference on the Interplay between Natural and Artificial Computation (IWINAC 2013), Lecture Notes in Computer Science, Volume 7930, pp. 225-234.
(Paper, Slides)
Boye Annfelt Høverstad, Axel Tidemann and Helge Langseth (2013):
Effects of data cleansing on load prediction algorithms.
Presented at the IEEE Symposium Series on Computational Intelligence, ISBN 978-1-4673-6002-9, pp. 93-100.
(Paper)
Axel Tidemann, Boye Annfelt Høverstad, Helge Langseth, and Pinar Öztürk (2013):
Effects of scale on load prediction algorithms.
Presented at the International Conference on Electricity Distribution (CIRED)
(Paper)
2012
Helge Langseth and Thomas D. Nielsen (2012):
A latent model for collaborative filtering
International Journal of Approximate Reasoning 53: 447-466, 2012.
(Early version, Paper)
Helge Langseth, Thomas D. Nielsen, Rafael Rumí, and
Antonio Salmerón (2012):
Mixtures of Truncated Basis Functions
International Journal of Approximate Reasoning 53: 212-227, 2012.
(Preprint, Paper,
Matlab code)
Helge Langseth, Thomas D. Nielsen, and
Antonio Salmerón (2012):
Learning Mixtures of Truncated Basis Functions from Data
Presented at the Sixth European Workshop on Probabilistic Graphical Models (PGM 2012),
DECSAI, University of Granada Publications, ISBN 978-84-15536-57-4, pp. 163-170
(Paper, Slides)
Helge Langseth, Thomas D. Nielsen, Rafael Rumí, and
Antonio Salmerón (2012):
Inference in hybrid Bayesian networks with Mixtures of Truncated Basis Functions
Presented at the Sixth European Workshop on Probabilistic Graphical Models (PGM 2012),
DECSAI, University of Granada Publications, ISBN 978-84-15536-57-4, pp. 171-178
(Paper)
2011
Tore Bruland, Agnar Aamodt and Helge Langseth (2011):
A Hybrid CBR and BN Architecture Refined through Data Analysis
In the proceedings of The 11th International Conference on
Intelligent Systems Design and Applications (ISDA), Cordoba, Spain,
November 22-24, 2011. IEEE conference proceedings, ISBN 978-1-4577-1675-1. Pages 906-913.
Tor Gunnar Houeland, Tore Bruland, Agnar Aamodt and Helge Langseth (2011):
Combining CBR and BN using metareasoning
In Frontiers in Artificial Intelligence and Applications, Volum 227:
The Eleventh Scandinavian Conference on Artificial Intelligence, Trondheim, Norway, May 24-26, 2011. Pages 189-191.
Terje N. Lilegraven, Arnt C. Wolden, Anders Kofod-Petersen and Helge Langseth (2011):
A design for a tourist CF system
In Frontiers in Artificial Intelligence and Applications, Volum 227:
The Eleventh Scandinavian Conference on Artificial Intelligence, Trondheim, Norway, May 24-26, 2011. Pages 193-194.
2010
Anders Kofod-Petersen and Helge Langseth (2010):
Tourist Without a Cause.
Presented at the Second Norwegian Artificial Intelligence Symposium, Gjøvik, Norway, November 22, 2010.
Tore Bruland, Agnar Aamodt, and Helge Langseth (2010):
Architectures case-based reasoning and Bayesian networks for clinical decision support.
Proceedings of IIP2010 - 6th International Conference on Intelligent Information Processing. IFIP Conference Series. Manchester, UK, October 13-16, 2010. Springer Verlag, 2010.
Antonio Fernández, Helge Langseth, Thomas D. Nielsen, and Antonio Salmerón (2010):
>
Parameter learning in MTE networks using incomplete data.
Presented at the Fifth European Workshop on Probabilistic Graphical Models (PGM 2010), HIIT Publications 2010-2, pp. 137-145
(Paper)
Anders Kofod-Petersen, Helge Langseth, and Agnar Aamodt (2010):
Explanations in Bayesian networks using provenance trough case-based reasoning.
Presented at the 18th International Conference on Case-Based Reasoning (ICCBR 2010), Workshop Provenanace-Aware Case-Based Reasoning - Application to Reasoning, Metareasoning, Maintenance and Explanation, pp. 79 - 86, 2010. IEEE Computer Society.
(Preprint)
Shengtong Zhong, Ana M. Martinez, Thomas D. Nielsen, and Helge Langseth (2010):
Towards a More ExpressiveModel for Dynamic Classification
Presented at the 23rd Florida Artificial Intelligence Research Society Conference, AAAI, Florida, USA.
(Preprint)
Helge Langseth, Thomas D. Nielsen, Rafael Rumí, and
Antonio Salmerón (2010):
Parameter Estimation for Mixtures of Truncated Exponentials
International Journal of Approximate Reasoning 51: 485-498, 2010.
(Preprint, Paper)
2009
Helge Langseth and Thomas D. Nielsen (2009):
A latent model for collaborative filtering
Technical Report 09-003, Department of Computer Science, Aalborg University
(Technical report,
Journal version)
Shengtong Zhong and Helge Langseth (2009):
Local-Global-Learning of Naive Bayesian Classifier
Presented at ICICIC'09.
Proceedings of the Fourth International Conference on Innovative Computing, Information and Control, IEEE Computing Society, pp. 278-281.
(Preprint, Paper)
Helge Langseth, Thomas D. Nielsen, Rafael Rumí, and
Antonio Salmerón (2009):
Maximum Likelihood Learning of Conditional MTE Distributions
Presented at ECSQARU 2009.
Springer Lecture Notes in Computer Science Volume 5590/2009, pp. 240-251
(Preprint,
Paper)
Helge Langseth and Thomas D. Nielsen (2009):
Latent Classification models for Binary data
Pattern Recognition 42:2724-2736, 2009.
(Preprint,
Paper, More information)
Helge Langseth, Thomas D. Nielsen, Rafael Rumí, and
Antonio Salmerón (2009):
Inference in Hybrid Bayesian Networks
Reliability Engineering and System Safety 94:1499-1509, 2009.
(Preprint, Paper)
2008
Helge Langseth (2008):
Bayesian networks in Reliability: The Good, The Bad, and The Ugly.
Advances in Mathematical Modeling for Reliability. IOS Press 2008 ISBN 978-1-58603-865-6.
(Latest draft, Slides)
Helge Langseth, Thomas D. Nielsen, Rafael Rumí, and
Antonio Salmerón (2008):
Parameter Estimation in Mixtures of Truncated Exponentials
Presented at PGM 08. (An extended version of the paper is published in IJAR.)
(Paper, Slides)
2007
Helge Langseth, Thomas D. Nielsen, Rafael Rumí, and
Antonio Salmerón (2007):
Maximum Likelihood vs. Least Squares for
Estimating Mixtures of Truncated Exponentials
Presented by Antonio Salmerón at INFORMS, 2007.
(Antonio's slides)
Helge Langseth (2007):
Bayesian networks in Reliability: The Good, The Bad, and The Ugly.
Presented at The fifth International Conference on
Mathematical Models in Reliability, MMR'07.
(This was a keynote speech, so the slides are nice, but the paper was more of a rush-job. An extended version of the paper was published later.)
Helge Langseth and Finn V. Jensen (2007):
Bayesian belief network computation for reliability.
In Encyclopedia of Statistics in Quality and Reliability (Wiley)
(Preprint,
Paper)
Helge Langseth and Luigi Portinale (2007):
Bayesian Networks in Reliability
Reliability
Engineering and System Safety 92(1), pp. 92-108, 2007.
Early version available as Technical Report No.
TR-INF-2005-04-01-UNIPMN, Dept. of Computer Science, University of
Eastern Piedmont "Amedeo Avogadro", Alessandria, Italy.
(Preprint, Paper)
Helge Langseth and Luigi Portinale (2007):
Applications of Bayesian Networks in Reliability Analysis
In
Ankush Mittal, Ashraf Kassim, Tele Tan (Eds.): Bayesian Network
Technologies: Applications and Graphical Models (Idea Group
Publishers, USA).
(Latest draft)
2006
Helge Langseth and Thomas D.
Nielsen (2006):
Classification
using Hierarchical Naive Bayes models
Machine
Learning 63(2), pp. 135-159, 2006.
Early version available as Technical Report
No. R-02-004, Dept. of Computer Science, Aalborg University.
(Preprint, Paper, Slides)
Helge Langseth and Bo
Henry Lindqvist (2006):
Competing risks for repairable systems: A data study.
Journal of Statistical Planning and Inference 136(5), pp.
1683-1700, 2006.
(Preprint, Paper)
Bo Henry Lindqvist, Bård Støve
and Helge Langseth (2006):
Modelling of dependence between critical failure and preventive maintenance:
The repair alert model.
Journal of Statistical Planning and Inference 136(5), pp.
1701-1717, 2006.
(Preprint, Paper)
Jørn Vatn and Helge Langseth (2006):
Estimation of Weibull parameters when the i.i.d. assumption does not hold
In Lars Pettersson and Kaisa Simola (Eds.): Ageing of Components and Systems, pp. 128-141 (European Safety, Reliability & Data Association, Høvik, Norway).
2005
Helge Langseth and Thomas D.
Nielsen (2005):
Latent
Classification Models
Machine
Learning 59(3), pp. 237-265, 2005.
Early version available as Technical Report No. R-04-020,
Dept. of Computer Science, Aalborg University.
(Preprint, Paper, Slides)
Bo Henry Lindqvist and
Helge Langseth (2005):
Statistical
Modelling and Inference for Component Failure Times under Preventive
Maintenance and Independent Censoring.
In: Modern
Statistical and Mathematical Methods in Reliability, pp 319-333
(World Scientific Publishing, Singapore).
(Preprint)
Per Hokstad, Helge Langseth, Bo Henry Lindqvist and
Jørn Vatn (2005):
Failure-Model and Maintenance Optimization for a Railway Line
International Journal of Performability
Engineering 1(1), pp. 51-64, 2005.
(Preprint)
2004
Helge Langseth (2004):
Data
Mining: My view
A really simplistic introduction to
Data Mining for undergraduate computer science students.
Helge Langseth (2004):
Bayesian
Networks in Reliability: Some recent developments
Presented at The fourth International Conference on
Mathematical Models in Reliability, MMR'04.
(Paper, Slides)
Thor
Bjørkvoll and Helge Langseth (2004):
The
Prioritization of Risk Reducing Measures in View of Uncertain
Cost/Benefits
Presented at The Seventh international
conference on probabilistic safety assessment and management, PSAM 7.
(Paper)
2003
Helge Langseth and Thomas D.
Nielsen (2003):
Fusion
of Domain Knowledge with Data for Structural Learning in Object
Oriented Domains
Journal of Machine Learning
Research 4, pp. 339-368.
(Paper)
Helge Langseth and Bo
Henry Lindqvist (2003):
Competing
risk combined with Imperfect repair: Some of the dirty
details
Presented at the Workshop for Competing Risk
(Slides)
Helge Langseth and Bo
Lindqvist (2003):
A
maintenance model for components exposed to several failure
mechanisms and imperfect repair.
In: Mathematical
and Statistical Methods in Reliability, pp 415-430 (World
Scientific Publishing, Singapore).
(Latest draft)
Helge Langseth and Finn V.
Jensen (2003):
Decision
Theoretic Troubleshooting of Coherent Systems.
Reliability
Engineering and System Safety 80(1), pp. 49-61, 2003.
(Preprint, Paper)
2002
Helge Langseth (2002):
Bayesian networks with
applications in reliability analysis
PhD thesis.
(Description, Single file)
Helge Langseth and Bo
Henry Lindqvist (2002):
Modelling
imperfect maintenance and repair of components under competing
risk
Presented at The Third International Conference on
Mathematical Models in Reliability, MMR'02.
(Paper,
Slides)
2001
Olav Bangsø,
Helge Langseth and Thomas D.
Nielsen (2001):
Structural
Learning in Object Oriented Domains.
Presented at The Fourteenth International Florida Artificial Intelligence
Research Society Conference, FLAIRS'01.
(Paper, Slides)
Helge Langseth and Finn
Verner Jensen (2001):
Heuristics
for two extensions of basic troubleshooting.
Presented at The Seventh Scandinavian Conference on Artificial
Intelligence, SCAI'01.
(Paper, Slides)
Helge Langseth and Olav
Bangsø (2001):
Parameter
Learning in Object Oriented Bayesian Networks.
Annals of Mathematics and Artificial Intelligence 32(1/4),
pp. 221-243, 2001.
(Preprint, Paper)
Finn V. Jensen, Uffe Kjærulff, Brian
Krisitiansen, Helge Langseth, Claus Scanning, Marta Vomlelova and Jiri Vomlel (2001):
The
SACSO methodology for troubleshooting complex
systems.
Artificial Intelligence for Engineering, Design, Analysis and
Manufacturing 15(4), pp. 321-333, 2001.
(Preprint, Paper)
Before 2000
Helge Langseth, Agnar Aamodt and
Ole Martin Winnem (1999):
Learning Retrieval Knowledge from Data.
Presented at The IJCAI Workshop on Automating the Construction
of Case Based Reasoners.
(Paper, Slides)
Helge Langseth (1999):
Modelling
maintenance for components under competing
risk.
Presented at The European Safety and
Reliability Conference (ESREL)
1999.
Agnar Aamodt
and Helge Langseth (1998):
Integrating
Bayesian Networks into Knowledge-Intensive
CBR.
Presented at The AAAI Workshop on Case-Based
Reasoning Integrations.
(Paper)
Helge Langseth (1998):
Analysis
of survival times using Bayesian networks (Slides).
Presented
at The European Safety and Reliability Conference (ESREL)
1998.
Helge Langseth, Knut Haugen and Helge Sandtorv (1998):
Analysis
of OREDA Data for Maintenance Optimisation.
Reliability
Engineering and System Safety 60(2), pp. 103-110, 1998.
(Preprint, Paper)
Helge Langseth and Bo
Henry Lindqvist (1998):
Uncertainty
Bounds for a Monotone Multistate System.
Probability in the Engineering and Informational Sciences
12(2), pp. 239-260, 1998.
(Preprint,
Paper)