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However, data sets used in this field are not comprehensive and the comparison works are not sufficient.
However, data sets of this immense size require special computing expertise to analyze and store information, as they are too large and too complex for conventional software and hardware.
However, data sets do not necessarily have to be complete to provide phylogenetic information.
However, data sets with precise information on the breed composition of animals are lacking.
However, data sets of the outcomes were too incomplete and therefore unsuitable for analysis.
However, data sets used in making predictions between two sample groups may potentially be susceptible to data over-fit.
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However, data set sizes have increased by orders of magnitude, thanks to sequencing technology platforms such as the Roche GS, Ion Torrent and the Illumina series [ 56], each with particular commercial characteristics (e.g. cost and sequencing data yield) [ 57].
However, those data sets are not in the scope of a classifier, but require regression techniques.
However, selected data sets consist of mainly projects which have mathematical functions.
However, large data sets may make it difficult to apply (Finkbeiner et al. 2006).
However, many data sets contain ordering information helpful for revealing patterns or for locating particular objects in hierarchies [6].
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