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| author | Vasil Zlatanov <v@skozl.com> | 2019-02-14 17:06:31 +0000 |
|---|---|---|
| committer | Vasil Zlatanov <v@skozl.com> | 2019-02-14 17:06:31 +0000 |
| commit | ddb42abe861dc88215f28fc5ec7528b906f250b1 (patch) | |
| tree | eaed7485d1317024a6193162d7ea94a319b21e8d /report | |
| parent | 3c5784b1fcd2321ab598b04757943a4b8be11e9c (diff) | |
| download | e4-vision-ddb42abe861dc88215f28fc5ec7528b906f250b1.tar.gz e4-vision-ddb42abe861dc88215f28fc5ec7528b906f250b1.tar.bz2 e4-vision-ddb42abe861dc88215f28fc5ec7528b906f250b1.zip | |
Move references before Appendix
Diffstat (limited to 'report')
| -rw-r--r-- | report/paper.md | 9 |
1 files changed, 5 insertions, 4 deletions
diff --git a/report/paper.md b/report/paper.md index 57cbf23..4d9afdf 100644 --- a/report/paper.md +++ b/report/paper.md @@ -152,6 +152,11 @@ For the Caltech_101 dataset, a RF codebook seems to be the most suitable method The `water_lilly` is the most misclassified class, both in 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. + +# References + +<div id="refs"></div> + \newpage # Appendix @@ -183,7 +188,3 @@ The Appendix section includes additional pictures to support some of the points \label{fig:p3_succ} \end{center} \end{figure} - -# References - - |