Mateusz Kobos

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  • Mateusz Kobos
  • 2009 International Multiconference on Computer…
  • 2009
A new classification algorithm based on combination of two independent kernel density estimators per class is proposed. Each estimator is characterized by a different bandwidth parameter. Combination of the estimators corresponds to viewing the data with different “resolutions”. The intuition behind the method is that combining different views(More)
We consider a problem of selection of parameters in a classifier based on the average of kernel density estimators where each estimator 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 ratio(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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