JStatCom

JStatCom is a software framework that makes it easy to integrate numerical procedures written in specialized programming languages, like Matlab, Gauss or Ox, with the Java world. Furthermore, it helps building Graphical User Interfaces (GUI) for mathematical procedures by providing sophisticated data management features that seamlessy interact with Java Swing components.


References in zbMATH (referenced in 61 articles )

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  1. Chakraborty, Saptarshi; Das, Swagatam: Simultaneous variable weighting and determining the number of clusters -- A weighted Gaussian means algorithm (2018)
  2. Muñoz, Mario A.; Villanova, Laura; Baatar, Davaatseren; Smith-Miles, Kate: Instance spaces for machine learning classification (2018)
  3. Nápoles, Gonzalo; Falcon, Rafael; Papageorgiou, Elpiniki; Bello, Rafael; Vanhoof, Koen: Rough cognitive ensembles (2017)
  4. Esmi, Estevão; Sussner, Peter; Sandri, Sandra: Tunable equivalence fuzzy associative memories (2016)
  5. Fernández, Alberto; Elkano, Mikel; Galar, Mikel; Sanz, José Antonio; Alshomrani, Saleh; Bustince, Humberto; Herrera, Francisco: Enhancing evolutionary fuzzy systems for multi-class problems: distance-based relative competence weighting with truncated confidences (DRCW-TC) (2016)
  6. Gámez, Juan Carlos; García, David; González, Antonio; Pérez, Raúl: Ordinal classification based on the sequential covering strategy (2016)
  7. Manukyan, Artür; Ceyhan, Elvan: Classification of imbalanced data with a geometric digraph family (2016)
  8. Ougiaroglou, Stefanos; Evangelidis, Georgios: Efficient editing and data abstraction by finding homogeneous clusters (2016)
  9. Paternain, D.; Bustince, H.; Pagola, M.; Sussner, P.; Kolesárová, A.; Mesiar, R.: Capacities and overlap indexes with an application in fuzzy rule-based classification systems (2016)
  10. van den Burg, Gerrit J. J.; Groenen, Patrick J. F.: GenSVM: a generalized multiclass support vector machine (2016)
  11. García, David; Gámez, Juan Carlos; González, Antonio; Pérez, Raúl: An interpretability improvement for fuzzy rule bases obtained by the iterative rule learning approach (2015)
  12. González, Sergio; Herrera, Francisco; García, Salvador: Monotonic random forest with an ensemble pruning mechanism based on the degree of monotonicity (2015) ioport
  13. Jackowski, Konrad: Adaptive splitting and selection algorithm for regression (2015) ioport
  14. López, Victoria; del Río, Sara; Benítez, José Manuel; Herrera, Francisco: Cost-sensitive linguistic fuzzy rule based classification systems under the MapReduce framework for imbalanced big data (2015) ioport
  15. Ralescu, Anca; Díaz, Irene; Rodríguez-Muñiz, Luis J.: A classification algorithm based on geometric and statistical information (2015)
  16. Reyes-Galaviz, Orion F.; Pedrycz, Witold: Granular fuzzy models: analysis, design, and evaluation (2015)
  17. Tomczak, Jakub M.; Ziȩba, Maciej: Probabilistic combination of classification rules and its application to medical diagnosis (2015)
  18. Wang, Zhe; Fan, Qi; Ke, Sheng; Gao, Daqi: Structural multiple empirical kernel learning (2015)
  19. Acilar, Ayşe Merve; Arslan, Ahmet: A novel approach for designing adaptive fuzzy classifiers based on the combination of an artificial immune network and a memetic algorithm (2014) ioport
  20. Antonelli, Michela; Ducange, Pietro; Marcelloni, Francesco: A fast and efficient multi-objective evolutionary learning scheme for fuzzy rule-based classifiers (2014)

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