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)
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)
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)
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)
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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