Titlebook: Artificial Neural Networks in Pattern Recognition; 5th INNS IAPR TC 3 G Nadia Mana,Friedhelm Schwenker,Edmondo Trentin Conference proceedin

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https://doi.org/10.1007/978-3-319-20866-4c signs in European countries share many similarities but also vary in colour, size, and depicted symbols, making it hard to obtain one general classifier with good performance in all countries. Training separate classifiers for each country requires huge amounts of labelled training data. A well-tr

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Teri Tibbett,Michael I. Jefferydemographic data from the 2010 United States census. The counties in these clusters are then analyzed for how they voted in the 2008 U.S. Presidential election, and political strategies are discussed that target demographically similar geographical regions based on ESOM results. The ESOM and .-means

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Permissive and Provocative Factors in FAS, of 89.9?%. Here, almost half of the misclassified letters are confusion pairs, such as .-. and .-.. This classification performance can be increased by decision fusion, using the sum rule, to 92.4?%.

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https://doi.org/10.1007/978-3-319-20866-4ome ineffective or even fail completely due to the occurrence of incorrectly labelled samples. To assure that self-training classifiers adapt themself correctly, advanced multi-classifier training methods like co-training are applied.

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Traffic Sign Classifier Adaption by Semi-supervised Co-trainingome ineffective or even fail completely due to the occurrence of incorrectly labelled samples. To assure that self-training classifiers adapt themself correctly, advanced multi-classifier training methods like co-training are applied.

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