Titlebook: Computer Recognition Systems; Proceedings of 4th I Marek Kurzyński,Edward Puchała,Andrzej żołnierek Conference proceedings 2005 Springer-Ve

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Margin-based Diversity Measures for Ensemble Classifiersfor three types of ensembles of linear classifiers. The tests show that these measures are better at predicting recognition accuracy than established diversity measures, such as . or disagreement measures, or entropy.

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Time Series Patterns Recognition with Genetic Algorithmsan one answer to processed data or no response at all. Early testing results, including prediction and fitting of simple time series with missing data amount ranging from 10 to 50 percent, are presented at the end of this paper.

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Feature Extraction with Wavelet Transformation for Statistical Object Recognitione different resolutions in the training phase. Experiments made on a real data set with 42240 images show that the recognition rates are much better using the resolution combination of the wavelet transformation.

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Some elements of potential theorye solved or circumvented either by novel and better procedures, or by a better understanding of their causes. Here, we will try to identify a number of open issues and define them as well as possible.

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Motivation of the local approachor learning in each boosting round. The new method achieves significantly better accuracy than both single FLD and FLD with boosting, with improvements reaching 6% in some cases. We show that the good performance can be attributed to higher diversity of the individual FLDs, as well as to the better generalization abilities.

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