Mateusz Kobos

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A new classification algorithm based on combination of kernel density estimators is introduced. The method combines the estima-tors with different bandwidths what can be interpreted as looking at the data with different " resolutions " which, in turn, potentially gives the algorithm an insight into the structure of the data. The bandwidths are adjusted(More)
We consider a problem of selection of parameters in a classi-fier based on the average of kernel density estimators where each estima-tor corresponds to a different data " resolution ". The selection is based on adjusting parameters of the estimators to minimize a substitute of the misclassification ratio. We experimentally compare the misclassification(More)
The Information Inference Framework presented in this paper provides a general-purpose suite of tools enabling the definition and execution of flexible and reliable data processing workflows whose nodes offer application-specific processing capabilities. The IIF is designed for the purpose of processing big data, and it is implemented on top of Apache(More)
—This paper presents a simple automatic system for small and middle Internet companies selling goods. The system combines temporal sales data with its geographical location and presents the resulting information on a map. Such an approach to data presentation should facilitate understanding of sales structure. This insight might be helpful in generating(More)
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