Institute of Information Theory and Automation

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Publication details

Knowledge Uncertainty and Composed Classifier

Journal Article

Klimešová Dana, Ocelíková E.

serial: International Journal of Circuits, Systems and Signal Processing vol.1, 2 (2007), p. 101-105

research: CEZ:AV0Z10750506

keywords: Boosting architecture, contextual modelling, composed classifier, knowledge management,, knowledge, uncertainty

abstract (eng):

The paper discuss the problem of wide context (temporal, spatial, local, objective, attribute oriented, relation oriented) as a tool to compensate and to decrease the uncertainty of data, classification and analytical process at all process to increase the information value of decision support. The contribution deals with a problem of creating the composed classifier with boosting architecture, whose components are composed of classifiers working with k - NN algorithm (k - th nearest neighbour).

abstract (cze):

Příspěvek je věnován problematice širokého kontextu z hlediska jeho možností kompenzovat neurčitost dat.


2012-12-21 16:10