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- W2296256670 abstract "This paper discusses possibilities how to use input filters to improve performance in ensemble of neural-networks-based classifiers. The proposed method is based on filtering of input vectors in the used training set, which minimize demands on data preprocessing. Our approach comes out from a technique called boosting, which is based on the principle of combining a large number of so-called weak classifiers into a strong classifier. In the experimental study, we verified that such classifiers are able to sufficiently classify the submitted data into predefined classes without knowledge of details of their significance." @default.
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- W2296256670 date "2015-01-01" @default.
- W2296256670 modified "2023-09-23" @default.
- W2296256670 title "Input Filters Implementing Diversity in Ensemble of Neural Networks" @default.
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- W2296256670 doi "https://doi.org/10.1007/978-3-319-19644-2_26" @default.
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