Turki Turki

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Instance reduction methods are popular methods that reduce the size of the datasets to possibly improve the classification accuracy. We present a method that reduces the size of the dataset based on the percentile of the dataset partitions which we call IPRed. We evaluate our and other popular instance reduction methods from a classification perspective by(More)
Programs based on hash tables and Burrows-Wheeler are very fast for mapping short reads to genomes but have low accuracy in the presence of mismatches and gaps. Such reads can be aligned accurately with the Smith-Waterman algorithm but it can take hours and days to map millions of reads even for bacteria genomes. We introduce a GPU program called MaxSSmap(More)
Ensemble methods such as AdaBoost are popular machine learning methods that create highly accurate classifier by combining the predictions from several classifiers. We present a parametrized method of AdaBoost that we call Top-k Parametrized Boost. We evaluate our and other popular ensemble methods from a classification perspective on several real datasets.(More)
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