Download Soft Computing in Computer and Information Science by Antoni Wiliński, Imed El Fray, Jerzy Pejaś PDF

By Antoni Wiliński, Imed El Fray, Jerzy Pejaś

This e-book provides a gently chosen and reviewed number of papers awarded through the nineteenth complicated desktops convention ACS-2014. The complicated computers convention targeted from its starting on tools and algorithms of synthetic intelligence. extra destiny introduced new components of curiosity referring to technical informatics with regards to delicate computing and a few extra technological features of computing device technological know-how equivalent to multimedia and special effects, software program engineering, net platforms, info safety and security or venture administration. those themes are represented within the current publication less than the kinds synthetic Intelligence, layout of knowledge and Multimedia platforms, details know-how defense and software program Technologies.

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On Geoscience and Remote Sensing journal, proposing a technique for fast calculation of moments (11) for successive windows via multiple integral images. , with 1 pixel shifts. 20 P. Kle˛sk et al. Fig. 3 Test errors and ROC curves for “metal detectors” Fig. 4 Test errors and ROC curves for “plastic detectors” for the normalized area under ROC obtained up to the FAR equal α. This shows how fast the ROC grows in its initial stage. Results at the “image-level of detail" (for whole C-scans) are reported in the Table 2.

5 -1 7 0 1 2 3 4 x 5 6 7 x Fig. 5 0 1 2 3 4 x 5 6 7 0 1 2 3 4 5 6 7 x Fig. 2) taking values from 0 to 2π . 2 and the number of nearest neighbours taken as 15. For comparison, on the right we can see the plot created for weighted kNN method. Figures 7 and 8 presents a cross-validation error of the model created for data also expanded to intervals as before. In Fig. 7 we can see a variation of the lower and upper bounds of the cross-validation error interval for different number of neighbours k taken into account.

Mini-models—local regression models for the function approximation learning. , et al. ) ICAISC 2012, Part II. LNCS, pp. 160–167. Springer, Heidelberg (2012) 18. : Evaluation of the mini-models robustness to data uncertainty with the application of the information-gap theory. , et al. ) ICAISC 2013, Part II. LNAI, pp. 230–241. Springer, Heidelberg (2013) 19. : INTLAB—INTerval LABoratory. Developements in Reliable Computing, pp. 77–104. Kluwer Academic Publishing, Dordrecht (1999) A Parametric Interval Approximation of Fuzzy Numbers Luca Anzilli and Gisella Facchinetti Abstract In this paper we present a parametric formulation of interval approximation of fuzzy numbers.

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