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| author | Vasil Zlatanov <v@skozl.com> | 2019-02-14 17:13:29 +0000 |
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| committer | Vasil Zlatanov <v@skozl.com> | 2019-02-14 17:13:29 +0000 |
| commit | e04112f5b85667b57dbb2680930f40f8e02daecc (patch) | |
| tree | ad0163dadb8814eceb17e145c40626bf0c3da042 | |
| parent | da43c2140963a1369ba0565b5d434df8b88fe78e (diff) | |
| download | e4-vision-e04112f5b85667b57dbb2680930f40f8e02daecc.tar.gz e4-vision-e04112f5b85667b57dbb2680930f40f8e02daecc.tar.bz2 e4-vision-e04112f5b85667b57dbb2680930f40f8e02daecc.zip | |
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| -rw-r--r-- | report/paper.md | 3 |
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diff --git a/report/paper.md b/report/paper.md index dc7345a..329efca 100644 --- a/report/paper.md +++ b/report/paper.md @@ -152,6 +152,9 @@ For the Caltech_101 dataset, a RF codebook seems to be the most suitable method The `water_lilly` is the most misclassified class, both for K-means and RF codebook (refer to figures \ref{fig:km_cm} and \ref{fig:p3_cm}). This indicates that the features obtained from the class do not provide for very discriminative splits, resulting in the prioritsation of other features in the first nodes of the decision trees. +All code/graphs and configurable scripts can be found on our repository: + +``git clone https://git.skozl.com/e4-vision/`` # References |