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

By Antoni Wiliński, Imed El Fray, Jerzy Pejaś

This ebook provides a gently chosen and reviewed selection of papers provided throughout the nineteenth complicated desktops convention ACS-2014. The complex desktops convention targeted from its starting on equipment and algorithms of synthetic intelligence. extra destiny introduced new parts of curiosity pertaining to technical informatics with regards to delicate computing and a few extra technological facets of machine technology akin to multimedia and special effects, software program engineering, net platforms, details safety and security or venture administration. those subject matters are represented within the current booklet less than the kinds synthetic Intelligence, layout of knowledge and Multimedia platforms, details expertise defense and software program Technologies.

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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 sensitivity. This operation was done in order to significantly decrease the number of false alarms in images, but still keeping high detectivity. 999, assuming an independence model for simplification. The following summarizing comments on the results can be formulated. Obviously, our data set is strongly imbalanced, favoring the negative class, and so are classification results.

It is sufficient to look to a usual fuzzy control system output that may be neither normal nor convex. In this paper we propose to substitute a given fuzzy number with an interval which has some properties like to be the nearest in some sense we describe. The results we present start from a paper of Grzegorzewski [10] in which he proposes the nearest interval to the original fuzzy number with respect to several distances. In particular we have focused on the distance introduced by Trutschnig et al.

In our case study, we worked with a fairly limited collection of 210 C-scans (from ≈1 m2 each), but we generated a large data set from it—with approximately 7 × 104 examples of 3D windows each described by over 15 thousand features. Our experiments showed that a commonly applied single-feature weak learner which approximates class conditional probabilities by normal distributions (popularly known as “real boost," NRB in our notation) was significantly outperformed by learners only slightly more refined—based on binning and shallow decision trees.

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