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| author | Vasil Zlatanov <v@skozl.com> | 2019-02-12 18:08:29 +0000 |
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| committer | Vasil Zlatanov <v@skozl.com> | 2019-02-12 18:08:29 +0000 |
| commit | b235059fd844b59d4cd22104af12f42c2ac0deb2 (patch) | |
| tree | 07c079404f304afcb8edd59ac34256358ea0bf1d /report | |
| parent | 8bb27a18fdb6468367d60ccbb4d639b5ae546e59 (diff) | |
| download | e4-vision-b235059fd844b59d4cd22104af12f42c2ac0deb2.tar.gz e4-vision-b235059fd844b59d4cd22104af12f42c2ac0deb2.tar.bz2 e4-vision-b235059fd844b59d4cd22104af12f42c2ac0deb2.zip | |
Add comment about randomness exection time
Diffstat (limited to 'report')
| -rw-r--r-- | report/paper.md | 4 |
1 files changed, 3 insertions, 1 deletions
diff --git a/report/paper.md b/report/paper.md index d74ea0d..25531e2 100644 --- a/report/paper.md +++ b/report/paper.md @@ -60,7 +60,9 @@ Random forests will select a random number of features on which to apply a weak \end{center} \end{figure} -## Weak Learners comparison +Changing the randomness parameter had no significant effect on execution time. This can partly be explained by the increased required tree depth to purify the training set. + +## Weak Learner comparison In figure \ref{fig:2pt} it is possible to notice an improvement in recognition accuracy by 1%, with the two pixels test, achieving better results than the axis-aligned counterpart. The two-pixels |