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- W2549803385 abstract "A study was performed on Naive-Bayes and Label Spreading methods applied in a spam filter as classifiers. In the testing procedure their ability to predict was observed and the results were compared in a McNemar test; leading to the discovery of the strengths and weaknesses of the chosen methods in a environment of varying training data. Though the results were inconclusive due to resource restrictions, the theory is discussed from various angles in order to provide a better understanding of the conditions that can lead to potentially different results between the chosen methods; opening up for improvement and further studies. The conclusion made of this study is that a significant difference exists in terms of ability to predict labels between the two classifiers. On a secondary note it is recommended to choose a classifier depending on available training data and computational power." @default.
- W2549803385 created "2016-11-30" @default.
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- W2549803385 date "2016-01-01" @default.
- W2549803385 modified "2023-09-27" @default.
- W2549803385 title "Analyzing the ability of Naive-Bayes and Label Spreading to predict labels with varying quantities of training data" @default.
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- W2549803385 hasPublicationYear "2016" @default.
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