Computer Information Systems and Industrial Management: 12th by Mieczysław A. Kłopotek (auth.), Khalid Saeed, Rituparna

By Mieczysław A. Kłopotek (auth.), Khalid Saeed, Rituparna Chaki, Agostino Cortesi, Sławomir Wierzchoń (eds.)

This publication constitutes the lawsuits of the twelfth IFIP TC eight overseas convention, CISIM 2013, held in Cracow, Poland, in September 2013. The forty four papers awarded during this quantity have been rigorously reviewed and chosen from over 60 submissions. they're equipped in topical sections on biometric and biomedical functions; development reputation and photo processing; numerous points of computing device safeguard, networking, algorithms, and commercial purposes. The e-book additionally comprises complete papers of a keynote speech and the invited talk.

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Additional resources for Computer Information Systems and Industrial Management: 12th IFIP TC8 International Conference, CISIM 2013, Krakow, Poland, September 25-27, 2013. Proceedings

Example text

Nowadays simple methods of data analysis are not sufficient for efficient management of an average enterprize, since for smart decisions the knowledge hidden in data is highly required, as which multiple classifier systems are recently the focus of intense research. Unfortunately the great disadvantage of traditional classification methods is that they ”assume” that statistical properties of the discovered concept (which model is predicted) are being unchanged. In real situation we could observe so-called concept drift, which could be caused by changes in the probabilities of classes or/and conditional probability distributions of classes.

Methods of combining multiple classifiers and their applications to handwriting recognition. IEEE Transactions on Systems, Man and Cybernetics 22, 418–435 (1992) 11. : Analysis of decision boundaries in linearly combined neural classifiers. Pattern Recognition 29, 341–348 (1996) 12. : Decision combination in multiple classifier systems. IEEE Trans. Pattern Anal. Mach. Intell. 16, 66–75 (1994) 13. : Bagging predictors. Mach. Learn. 24, 123–140 (1996) 14. : The strength of weak learnability. Mach.

Bartkowiak and R. Zimroz Discriminant Functions Using Kernels Kernel methods may be defined and used in a number of ways, see for example [15, 13, 14, 6–8]. One might say shortly that this is canonical discriminant analysis carried out in an extended space F obtained by a non-linear mapping of the original data. The applied mapping takes into account various nonlinear relations between the observed variables which makes that in the extended feature space F the classical algorithms (like CDA) become more powerful.

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