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Semantic ScholarThomas D. Nielsen
Thomas Dyhre Nielsen
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2010 – today
- 2016
[j30]Jacinto Arias, José A. Gámez, Thomas D. Nielsen, José Miguel Puerta:
A scalable pairwise class interaction framework for multidimensional classification. Int. J. Approx. Reasoning 68: 194-210 (2016)- [j29]Manuel Luque, Thomas D. Nielsen, Finn Verner Jensen:
Anytime Decision Making Based on Unconstrained Influence Diagrams. Int. J. Intell. Syst. 31(4): 379-398 (2016) - [j28]Hua Mao, Yingke Chen, Manfred Jaeger, Thomas D. Nielsen, Kim G. Larsen, Brian Nielsen:
Learning deterministic probabilistic automata from a model checking perspective. Machine Learning 105(2): 255-299 (2016) - [c28]Antonio Salmerón, Anders L. Madsen, Frank Jensen, Helge Langseth, Thomas D. Nielsen, Darío Ramos-López, Ana M. Martínez, Andrés R. Masegosa:
Parallel Filter-Based Feature Selection Based on Balanced Incomplete Block Designs. ECAI 2016: 743-750 - [c27]Andrés R. Masegosa, Ana M. Martínez, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón, Darío Ramos-López, Anders L. Madsen:
d-VMP: Distributed Variational Message Passing. Probabilistic Graphical Models 2016: 321-332 - [c26]Darío Ramos-López, Antonio Salmerón, Rafael Rumí, Ana M. Martínez, Thomas D. Nielsen, Andrés R. Masegosa, Helge Langseth, Anders L. Madsen:
Scalable MAP inference in Bayesian networks based on a Map-Reduce approach. Probabilistic Graphical Models 2016: 415-425 - 2015
- [j27]Helge Langseth, Thomas D. Nielsen:
Scalable learning of probabilistic latent models for collaborative filtering. Decision Support Systems 74: 1-11 (2015) - [j26]Gherardo Varando, Pedro L. López-Cruz, Thomas D. Nielsen, Pedro Larrañaga, Concha Bielza:
Conditional Density Approximations with Mixtures of Polynomials. Int. J. Intell. Syst. 30(3): 236-264 (2015) - [c25]Anders L. Madsen, Frank Jensen, Antonio Salmerón, Helge Langseth, Thomas D. Nielsen:
Parallelisation of the PC Algorithm. CAEPIA 2015: 14-24 - [c24]Antonio Salmerón, Darío Ramos-López, Hanen Borchani, Ana M. Martínez, Andrés R. Masegosa, Antonio Fernández, Helge Langseth, Anders L. Madsen, Thomas D. Nielsen:
Parallel Importance Sampling in Conditional Linear Gaussian Networks. CAEPIA 2015: 36-46 - [c23]Antonio Salmerón, Rafael Rumí, Helge Langseth, Anders L. Madsen, Thomas D. Nielsen:
MPE Inference in Conditional Linear Gaussian Networks. ECSQARU 2015: 407-416 - [c22]Hanen Borchani, Ana M. Martínez, Andrés R. Masegosa, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón, Antonio Fernández, Anders L. Madsen, Ramón Sáez:
Modeling Concept Drift: A Probabilistic Graphical Model Based Approach. IDA 2015: 72-83 - [c21]Hanen Borchani, Ana M. Martínez, Andrés R. Masegosa, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón, Antonio Fernández, Anders L. Madsen, Ramón Sáez:
Dynamic Bayesian modeling for risk prediction in credit operations. SCAI 2015: 17-26 - 2014
- [j25]Andrés Cano, Manuel Gómez-Olmedo, Thomas Dyhre Nielsen:
Special Issue on PGM-2012. Int. J. Approx. Reasoning 55(4): 925 (2014) - [j24]Helge Langseth, Thomas D. Nielsen, Inmaculada Pérez-Bernabé, Antonio Salmerón:
Learning mixtures of truncated basis functions from data. Int. J. Approx. Reasoning 55(4): 940-956 (2014) - [j23]Shengtong Zhong, Helge Langseth, Thomas Dyhre Nielsen:
A classification-based approach to monitoring the safety of dynamic systems. Rel. Eng. & Sys. Safety 121: 61-71 (2014) - [c20]Thomas D. Nielsen, Sigve Hovda, Antonio Fernández, Helge Langseth, Anders L. Madsen, Andrés R. Masegosa, Antonio Salmerón:
Requirement Engineering for a Small Project with Pre-Specified Scope. NIK 2014 - [c19]Jacinto Arias, José A. Gámez, Thomas D. Nielsen, Jose Miguel Puerta:
A Pairwise Class Interaction Framework for Multilabel Classification. Probabilistic Graphical Models 2014: 17-32 - [c18]Anders L. Madsen, Frank Jensen, Antonio Salmerón, Martin Karlsen, Helge Langseth, Thomas D. Nielsen:
A New Method for Vertical Parallelisation of TAN Learning Based on Balanced Incomplete Block Designs. Probabilistic Graphical Models 2014: 302-317 - 2013
- [j22]Finn Verner Jensen, Thomas Dyhre Nielsen:
Probabilistic decision graphs for optimization under uncertainty. Annals OR 204(1): 223-248 (2013) - [c17]Pedro L. López-Cruz, Thomas D. Nielsen, Concha Bielza, Pedro Larrañaga:
Learning Mixtures of Polynomials of Conditional Densities from Data. CAEPIA 2013: 363-372 - [e2]Manfred Jaeger, Thomas Dyhre Nielsen, Paolo Viappiani:
Twelfth Scandinavian Conference on Artificial Intelligence, SCAI 2013, Aalborg, Denmark, November 20-22, 2013. Frontiers in Artificial Intelligence and Applications 257, IOS Press 2013, ISBN 978-1-61499-329-2 [contents] - [i3]Thomas D. Nielsen, Finn Verner Jensen:
Representing and Solving Asymmetric Bayesian Decision Problems. CoRR abs/1301.3879 (2013) - [i2]Thomas D. Nielsen, Pierre-Henri Wuillemin, Finn Verner Jensen, Uffe Kjærulff:
Using ROBDDs for Inference in Bayesian Networks with Troubleshooting as an Example. CoRR abs/1301.3880 (2013) - [i1]
- 2012
- [j21]Helge Langseth, Thomas D. Nielsen, Rafael Rumí, Antonio Salmerón:
Mixtures of truncated basis functions. Int. J. Approx. Reasoning 53(2): 212-227 (2012) - [j20]Helge Langseth, Thomas Dyhre Nielsen:
A latent model for collaborative filtering. Int. J. Approx. Reasoning 53(4): 447-466 (2012) - [c16]Yingke Chen, Thomas Dyhre Nielsen:
Active Learning of Markov Decision Processes for System Verification. ICMLA (2) 2012: 289-294 - [c15]Yingke Chen, Hua Mao, Manfred Jaeger, Thomas Dyhre Nielsen, Kim Guldstrand Larsen, Brian Nielsen:
Learning Markov Models for Stationary System Behaviors. NASA Formal Methods 2012: 216-230 - [c14]Hua Mao, Yingke Chen, Manfred Jaeger, Thomas D. Nielsen, Kim G. Larsen, Brian Nielsen:
Learning Markov Decision Processes for Model Checking. QFM 2012: 49-63 - 2011
- [j19]Finn Verner Jensen, Thomas D. Nielsen:
Probabilistic decision graphs for optimization under uncertainty. 4OR 9(1): 1-28 (2011) - [c13]Hua Mao, Yingke Chen, Manfred Jaeger, Thomas D. Nielsen, Kim G. Larsen, Brian Nielsen:
Learning Probabilistic Automata for Model Checking. QEST 2011: 111-120 - 2010
- [j18]Manfred Jaeger, Thomas D. Nielsen:
Special Issue on PGM-2008. Int. J. Approx. Reasoning 51(5): 473 (2010) - [j17]Helge Langseth, Thomas D. Nielsen, Rafael Rumí, Antonio Salmerón:
Parameter estimation and model selection for mixtures of truncated exponentials. Int. J. Approx. Reasoning 51(5): 485-498 (2010) - [c12]Shengtong Zhong, Ana M. Martínez, Thomas D. Nielsen, Helge Langseth:
Towards a More Expressive Model for Dynamic Classification. FLAIRS Conference 2010
2000 – 2009
- 2009
- [j16]Kristian S. Ahlmann-Ohlsen, Finn Verner Jensen, Thomas D. Nielsen, Ole Pedersen, Marta Vomlelová:
A comparison of two approaches for solving unconstrained influence diagrams. Int. J. Approx. Reasoning 50(1): 153-173 (2009) - [j15]Helge Langseth, Thomas D. Nielsen:
Latent classification models for binary data. Pattern Recognition 42(11): 2724-2736 (2009) - [j14]Helge Langseth, Thomas D. Nielsen, Rafael Rumí, Antonio Salmerón:
Inference in hybrid Bayesian networks. Rel. Eng. & Sys. Safety 94(10): 1499-1509 (2009) - [c11]Helge Langseth, Thomas D. Nielsen, Rafael Rumí, Antonio Salmerón:
Maximum Likelihood Learning of Conditional MTE Distributions. ECSQARU 2009: 240-251 - 2008
- [j13]Søren Holbech Nielsen, Thomas D. Nielsen:
Adapting Bayes network structures to non-stationary domains. Int. J. Approx. Reasoning 49(2): 379-397 (2008) - 2007
- [j12]Thomas D. Nielsen, Finn Verner Jensen:
On-line alert systems for production plants: A conflict based approach. Int. J. Approx. Reasoning 45(2): 255-270 (2007) - 2006
- [j11]Thomas D. Nielsen, Jean-Yves Jaffray:
Dynamic decision making without expected utility: An operational approach. European Journal of Operational Research 169(1): 226-246 (2006) - [j10]Finn Verner Jensen, Thomas D. Nielsen, Prakash P. Shenoy:
Sequential influence diagrams: A unified asymmetry framework. Int. J. Approx. Reasoning 42(1-2): 101-118 (2006) - [j9]Helge Langseth, Thomas D. Nielsen:
Classification using Hierarchical Naïve Bayes models. Machine Learning 63(2): 135-159 (2006) - [c10]Jens A. Hansen, Thomas D. Nielsen, Henrik Schiøler:
A COTS framework for sensor fusion using dynamic bayesian networks in livestock production. CAINE 2006: 41-47 - [c9]Jens A. Hansen, Thomas D. Nielsen, Henrik Schiøler:
Sensor Fusion Using Dynamic Bayesian Networks in Livestock Production Buildings. CIMCA/IAWTIC 2006: 215 - [c8]Søren Holbech Nielsen, Thomas D. Nielsen:
Adapting Bayes Network Structures to Non-stationary Domains. Probabilistic Graphical Models 2006: 223-230 - 2005
- [j8]Thomas D. Nielsen, Nevin Lianwen Zhang:
Special Issue on ECSQARU-2003: The Seventh European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty: Message from the Guest Editors. Int. J. Approx. Reasoning 38(3): 215-216 (2005) - [j7]Helge Langseth, Thomas D. Nielsen:
Latent Classification Models. Machine Learning 59(3): 237-265 (2005) - [c7]Thomas D. Nielsen, Finn Verner Jensen:
Alert Systems for Production Plants: A Methodology Based on Conflict Analysis. ECSQARU 2005: 76-87 - 2004
- [j6]Thomas D. Nielsen, Finn Verner Jensen:
Learning a decision maker's utility function from (possibly) inconsistent behavior. Artif. Intell. 160(1-2): 53-78 (2004) - [j5]Nevin Lianwen Zhang, Thomas D. Nielsen, Finn Verner Jensen:
Latent variable discovery in classification models. Artificial Intelligence in Medicine 30(3): 283-299 (2004) - 2003
- [j4]Thomas D. Nielsen, Finn Verner Jensen:
Representing and Solving Asymmetric Decision Problems. International Journal of Information Technology and Decision Making 2(2): 217-263 (2003) - [j3]Helge Langseth, Thomas D. Nielsen:
Fusion of Domain Knowledge with Data for Structural Learning in Object Oriented Domains. Journal of Machine Learning Research 4: 339-368 (2003) - [j2]Thomas D. Nielsen, Finn Verner Jensen:
Sensitivity analysis in influence diagrams. IEEE Trans. Systems, Man, and Cybernetics, Part A 33(2): 223-234 (2003) - [e1]Thomas D. Nielsen, Nevin Lianwen Zhang:
Symbolic and Quantitative Approaches to Reasoning with Uncertainty, 7th European Conference, ECSQARU 2003, Aalborg, Denmark, July 2-5, 2003. Proceedings. Lecture Notes in Computer Science 2711, Springer 2003, ISBN 3-540-40494-5 [contents] - 2002
- [j1]Thomas D. Nielsen:
Decomposition of influence diagrams. Journal of Applied Non-Classical Logics 12(2): 135-150 (2002) - 2001
- [c6]
- [c5]Olav Bangsø, Helge Langseth, Thomas D. Nielsen:
Structural Learning in Object Oriented Domains. FLAIRS Conference 2001: 340-344 - [c4]Thomas D. Nielsen, Finn Verner Jensen:
Cutting Influence Diagrams Down to the Core. SCAI 2001: 159-160 - 2000
- [c3]Thomas D. Nielsen, Finn Verner Jensen:
Representing and Solving Asymmetric Bayesian Decision Problems. UAI 2000: 416-425 - [c2]Thomas D. Nielsen, Pierre-Henri Wuillemin, Finn Verner Jensen, Uffe Kjærulff:
Using ROBDDs for Inference in Bayesian Networks with Troubleshooting as an Example. UAI 2000: 426-435
1990 – 1999
- 1999
- [c1]
Coauthor Index
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last updated on 2016-10-13 04:42 CEST by the dblp team