Statistical Pattern Recognition Toolbox. This toolbox implements a selection of statistical pattern recognition methods described in the monograph M.I. Schlesinger, V. Hlavac: Ten lectures on the statistical and structural pattern recognition, Kluwer Academic Publishers, 2002 , rather it implements the first part of the monograph which deals with feature based statistical pattern recognition methods. The toolbox is still being developed and new implemented methods (see implemeted methods) go beyond the contents of the monograph. The toolbox should help to understand relevant algorithms from the monograph and to demonstrate their functionality. The visualization of the process leading to the solution and experimentation feasibility is stressed for this reason. The demonstrator environment is provided that allows the user to choose different algorithms, compare their behavior, provides tools to control the algorithm run interactively and creates synthetic input data or uses real ones. The implemented method can be used for real applications as well.

References in zbMATH (referenced in 13 articles )

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  1. Pourhabib, Arash; Mallick, Bani K.; Ding, Yu: Absent data generating classifier for imbalanced class sizes (2015)
  2. Guo, Liqiang; Dai, Ming; Zhu, Ming: Quaternion moment and its invariants for color object classification (2014) ioport
  3. Zhou, Ligang; Lai, Kin Keung; Yen, Jerome: Empirical models based on features ranking techniques for corporate financial distress prediction (2012)
  4. Shi, Fanhuai; Xi, Yongjian; Li, Xiaoling; Duan, Ye: An automation system of rooftop detection and 3D building modeling from aerial images (2011) ioport
  5. Alpak, Faruk O.; Barton, Mark D.; Caers, Jef: A flow-based pattern recognition algorithm for rapid quantification of geologic uncertainty (2010)
  6. Fontenla-Romero, Oscar; Guijarro-Berdiñas, Bertha; Pérez-Sánchez, Beatriz; Alonso-Betanzos, Amparo: A new convex objective function for the supervised learning of single-layer neural networks (2010)
  7. Lu, Zhao; Liang, Lily Rui; Song, Gangbing; Wang, Shufang: Polychotomous kernel Fisher discriminant via top-down induction of binary tree (2010)
  8. Winter, Lahiruka; Motai, Yuichi; Docef, Alen: On-line versus off-line accelerated kernel feature analysis: application to computer-aided detection of polyps in CT colonography (2010)
  9. Chen, Bo; Liu, Hongwei; Bao, Zheng: A kernel optimization method based on the localized kernel Fisher criterion (2008)
  10. Mutch, Jim; Lowe, David G.: Object class recognition and localization using sparse features with limited receptive fields (2008) ioport
  11. Sarma, Pallav; Durlofsky, Louis J.; Aziz, Khalid: Kernel principal component analysis for efficient, differentiable parametrization of multipoint geostatistics (2008)
  12. Lu, Zhao; Lin, Feng; Ying, Hao: Design of decision tree via kernelized hierarchical clustering for multiclass support vector machines (2007)
  13. Bozdogan, Hamparsum; Camillo, Furio; Liberati, Caterina: On the choice of the kernel function in kernel discriminant analysis using information complexity (2006)