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Johan Schubert
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2020 – today
- 2022
- [c21]Johan Schubert, Ulrika Wickenberg Bolin:
Cluster Management of Scientific Literature in HSTOOL. ICMLA 2022: 390-395
2010 – 2019
- 2019
- [c20]Maja Karasalo, Johan Schubert:
Developing Horizon Scanning Methods for the Discovery of Scientific Trends. ICDAR 2019: 1055-1062 - 2018
- [j14]Irfan Younas
, Farzad Kamrani, Maryam Bashir
Efficient genetic algorithms for optimal assignment of tasks to teams of agents. Neurocomputing 314: 409-428 (2018) - 2017
- [j13]Johan Schubert
Counter-deception in information fusion. Int. J. Approx. Reason. 91: 152-159 (2017) - 2016
- [c19]Johan Schubert:
Entropy-Based Counter-Deception in Information Fusion. BELIEF 2016: 174-181 - 2014
- [c18]Johan Schubert:
Partial Ranking by Incomplete Pairwise Comparisons Using Preference Subsets. Belief Functions 2014: 190-198 - [c17]Johan Schubert, Pontus Hörling:
Preference-based Monte Carlo weight assignment for multiple-criteria decision making in defense planning. FUSION 2014: 1-8 - [c16]Johan Schubert, Pontus Hörling:
A comparative study between using A*-search, Latin Hypercube and genetic algorithms in design of experiment for simulation of military operational plans. SMC 2014: 365-372 - 2013
- [c15]Irfan Younas
Solving Battalion Rescheduling Problem Using Multi-objective Genetic Algorithms. AsiaSim 2013: 93-104 - [c14]Johan Schubert, Anna Linderhed:
Decision support from learning multiple boundaries on military operational plans from simulation data. FUSION 2013: 1204-1213 - [c13]Irfan Younas
Using Genetic Algorithms in Effects-Based Planning. SMC 2013: 438-443 - [c12]Hirad Asadi, Johan Schubert:
A stochastic discrete event simulator for effects-based planning. WSC 2013: 2842-2853 - 2012
- [j12]Johan Schubert
Constructing and evaluating alternative frames of discernment. Int. J. Approx. Reason. 53(2): 176-189 (2012) - [c11]Johan Schubert
The Internal Conflict of a Belief Function. Belief Functions 2012: 169-177 - 2011
- [j11]Johan Schubert
Conflict management in Dempster-Shafer theory using the degree of falsity. Int. J. Approx. Reason. 52(3): 449-460 (2011) - [c10]Johan Schubert
Learning boundaries on military operational plans from simulation data. SMC 2011: 1325-1332 - 2010
- [c9]Johan Schubert:
Multi-level subjective effects-based assessment. FUSION 2010: 1-8 - [c8]Johan Schubert
Constructing Multiple Frames of Discernment for Multiple Subproblems. IPMU (1) 2010: 189-198 - [c7]Johan Schubert
Simulation-based decision support for effects-based planning. SMC 2010: 636-645
2000 – 2009
- 2009
- [c6]Johan Schubert, Pontus Svenson, Christian Mårtenson:
System prediction combining state estimation with an evidential influence diagram. FUSION 2009: 428-435 - 2008
- [j10]Johan Schubert
Clustering decomposed belief functions using generalized weights of conflict. Int. J. Approx. Reason. 48(2): 466-480 (2008) - [c5]Johan Schubert:
Subjective Effects-Based Assessment. FUSION 2008: 1-8 - 2007
- [j9]Simon Ahlberg, Pontus Hörling, Katarina Johansson, Karsten Jöred, Hedvig Kjellström
An information fusion demonstrator for tactical intelligence processing in network-based defense. Inf. Fusion 8(1): 84-107 (2007) - 2006
- [j8]Per Svensson, Johan Schubert
FUSION 2004: The seventh international conference on information fusion. Inf. Fusion 7(4): 342-345 (2006) - [c4]Johan Schubert, John Cantwell:
Computationally Efficient Multiple Hypothesis Association of Intelligence Reports. FUSION 2006: 1-6 - 2004
- [j7]Johan Schubert
Clustering belief functions based on attracting and conflicting metalevel evidence using Potts spin mean field theory. Inf. Fusion 5(4): 309-318 (2004) - [i22]Hedvig Sidenbladh, Pontus Svenson, Johan Schubert:
Comparing Multi-Target Trackers on Different Force Unit Levels. CoRR cs.AI/0411071 (2004) - 2003
- [i21]Johan Schubert:
On Nonspecific Evidence. CoRR cs.AI/0305013 (2003) - [i20]Ulla Bergsten, Johan Schubert:
Dempster's Rule for Evidence Ordered in a Complete Directed Acyclic Graph. CoRR cs.AI/0305014 (2003) - [i19]Johan Schubert:
Finding a Posterior Domain Probability Distribution by Specifying Nonspecific Evidence. CoRR cs.AI/0305015 (2003) - [i18]Johan Schubert:
Cluster-based Specification Techniques in Dempster-Shafer Theory. CoRR cs.AI/0305017 (2003) - [i17]Johan Schubert:
Cluster-based Specification Techniques in Dempster-Shafer Theory for an Evidential Intelligence Analysis of MultipleTarget Tracks (Thesis Abstract). CoRR cs.AI/0305018 (2003) - [i16]Johan Schubert:
On rho in a Decision-Theoretic Apparatus of Dempster-Shafer Theory. CoRR cs.AI/0305019 (2003) - [i15]Johan Schubert:
Specifying nonspecific evidence. CoRR cs.AI/0305020 (2003) - [i14]Johan Schubert:
Creating Prototypes for Fast Classification in Dempster-Shafer Clustering. CoRR cs.AI/0305021 (2003) - [i13]Ulla Bergsten, Johan Schubert, Per Svensson:
Applying Data Mining and Machine Learning Techniques to Submarine Intelligence Analysis. CoRR cs.AI/0305022 (2003) - [i12]Johan Schubert:
Fast Dempster-Shafer clustering using a neural network structure. CoRR cs.AI/0305023 (2003) - [i11]Johan Schubert:
A neural network and iterative optimization hybrid for Dempster-Shafer clustering. CoRR cs.AI/0305024 (2003) - [i10]Johan Schubert:
Simultaneous Dempster-Shafer clustering and gradual determination of number of clusters using a neural network structure. CoRR cs.AI/0305025 (2003) - [i9]Johan Schubert:
Fast Dempster-Shafer clustering using a neural network structure. CoRR cs.AI/0305026 (2003) - [i8]Johan Schubert:
Managing Inconsistent Intelligence. CoRR cs.AI/0305027 (2003) - [i7]Mats Bengtsson, Johan Schubert:
Dempster-Shafer clustering using Potts spin mean field theory. CoRR cs.AI/0305028 (2003) - [i6]John Cantwell, Johan Schubert, Johan Walter:
Conflict-based Force Aggregation. CoRR cs.AI/0305029 (2003) - [i5]Johan Schubert:
Reliable Force Aggregation Using a Refined Evidence Specification from Dempster-Shafer Clustering. CoRR cs.AI/0305030 (2003) - [i4]Johan Schubert:
Clustering belief functions based on attracting and conflicting metalevel evidence. CoRR cs.AI/0305031 (2003) - [i3]Johan Schubert:
Robust Report Level Cluster-to-Track Fusion. CoRR cs.AI/0305032 (2003) - [i2]Ulla Bergsten, Johan Schubert, Per Svensson:
Beslutstödssystemet Dezzy - en översikt. CoRR cs.AI/0305033 (2003) - [i1]Johan Schubert:
Evidential Force Aggregation. CoRR cs.AI/0309025 (2003) - 2001
- [j6]Mats Bengtsson, Johan Schubert:
Dempster-Shafer clustering using Potts spin mean field theory. Soft Comput. 5(3): 215-228 (2001)
1990 – 1999
- 1997
- [c3]Johan Schubert
Creating Prototypes for Fast Classification in Dempster-Shafer Clustering. ECSQARU-FAPR 1997: 525-535 - [c2]Ulla Bergsten, Johan Schubert, Per Svensson:
Applying Data Mining and Machine Learning Techniques to Submarine Intelligence Analysis. KDD 1997: 127-130 - 1996
- [j5]Johan Schubert:
Specifying nonspecific evidence. Int. J. Intell. Syst. 11(8): 525-563 (1996) - 1995
- [j4]Johan Schubert
On varrho in a decision-theoretic apparatus of Dempster-Shafer theory. Int. J. Approx. Reason. 13(3): 185-200 (1995) - [j3]Johan Schubert
Finding a posterior Domain Probability Distribution by Specifying Nonspecific Evidence. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 3(2): 163-186 (1995) - [c1]Johan Schubert
Cluster-based Specification Techniques in Dempster-Shafer Theory. ECSQARU 1995: 395-404 - 1993
- [j2]Ulla Bergsten, Johan Schubert
Dempster's rule for evidence ordered in a complete directed acyclic graph. Int. J. Approx. Reason. 9(1): 37-73 (1993) - [j1]Johan Schubert
On nonspecific evidence. Int. J. Intell. Syst. 8(6): 711-725 (1993)
Coauthor Index
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