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 79 articles )

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  1. Wu, Chengyuan; Ren, Shiquan; Wu, Jie; Xia, Kelin: Discrete Morse theory for weighted simplicial complexes (2020)
  2. Blachnik, Marcin: Ensembles of instance selection methods: a comparative study (2019)
  3. Dubnov, Yuriĭ A.: Entropy-based estimation in classification problems (2019)
  4. Panagopoulos, Orestis P.; Xanthopoulos, Petros; Razzaghi, Talayeh; Şeref, Onur: Relaxed support vector regression (2019)
  5. Zhang, Xueying; Li, Ruixian; Zhang, Bo; Yang, Yunxiang; Guo, Jing; Ji, Xiang: An instance-based learning recommendation algorithm of imbalance handling methods (2019)
  6. Chakraborty, Saptarshi; Das, Swagatam: Simultaneous variable weighting and determining the number of clusters -- a weighted Gaussian means algorithm (2018)
  7. dos Santos, Alex Santana; Valle, Marcos Eduardo: Max-plus and min-plus projection autoassociative morphological memories and their compositions for pattern classification (2018)
  8. Eichner, Martin (ed.); Halloran, M. Elizabeth (ed.); O’Neill, Philip D. (ed.): Design and analysis of infectious disease studies. Abstracts from the workshop held February 18--24, 2018 (2018)
  9. Livieris, Ioannis E.; Kanavos, Andreas; Tampakas, Vassilis; Pintelas, Panagiotis: An auto-adjustable semi-supervised self-training algorithm (2018)
  10. Muñoz, Mario A.; Villanova, Laura; Baatar, Davaatseren; Smith-Miles, Kate: Instance spaces for machine learning classification (2018)
  11. Nayak, Janmenjoy; Naik, Bighnaraj: A novel honey-bees mating optimization approach with higher order neural network for classification (2018)
  12. Boley, Mario; Goldsmith, Bryan R.; Ghiringhelli, Luca M.; Vreeken, Jilles: Identifying consistent statements about numerical data with dispersion-corrected subgroup discovery (2017)
  13. Gong, Joonho; Kim, Hyunjoong: Rhsboost: improving classification performance in imbalance data (2017)
  14. Gursoy, Mehmet Emre; Inan, Ali; Nergiz, Mehmet Ercan; Saygin, Yucel: Differentially private nearest neighbor classification (2017)
  15. Koziarski, Michał; Wożniak, Michał: CCR: a combined cleaning and resampling algorithm for imbalanced data classification (2017)
  16. Nápoles, Gonzalo; Falcon, Rafael; Papageorgiou, Elpiniki; Bello, Rafael; Vanhoof, Koen: Rough cognitive ensembles (2017)
  17. Esmi, Estevão; Sussner, Peter; Sandri, Sandra: Tunable equivalence fuzzy associative memories (2016)
  18. 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)
  19. Gámez, Juan Carlos; García, David; González, Antonio; Pérez, Raúl: Ordinal classification based on the sequential covering strategy (2016)
  20. Lemmerich, Florian; Atzmueller, Martin; Puppe, Frank: Fast exhaustive subgroup discovery with numerical target concepts (2016)

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