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authorNiklas Halle <niklas@niklashalle.net>2021-02-21 21:55:28 +0100
committerNiklas Halle <niklas@niklashalle.net>2021-02-21 21:55:28 +0100
commit275fa376ad4e89775bbb68a5dd9ef9af7a807fbd (patch)
treedf21d1f94b3050302a60396080c76791549a8f54
parenta9b8d43639d3e386915047212973485dc836790f (diff)
downloadbachelor_thesis-275fa376ad4e89775bbb68a5dd9ef9af7a807fbd.tar.gz
bachelor_thesis-275fa376ad4e89775bbb68a5dd9ef9af7a807fbd.zip
now that should be it for tonight
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\begin{document}
\maketitle
+ \unsure{The title is probably too long / detailed?}
\section{Purpose}
We want to compare the performance of “traditional” algorithms to find specific graph properties with the performance of a machine learning-based approach centred around graph embedding (on the grounds of random walks (\ntv)).\\Evaluation shall include speed, accuracy, stability and scalability \improvement{more/less factors to compare2?} in order to find whether certain problems might benefit from such a machine learning based approach. \improvement{Does that cover it?}