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author | Vasil Zlatanov <v@skozl.com> | 2018-12-13 14:37:28 +0000 |
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committer | Vasil Zlatanov <v@skozl.com> | 2018-12-13 14:37:28 +0000 |
commit | 593e701b22513809816359f14a5045528cc50bef (patch) | |
tree | cfc7b00f730bc9f93d00a321c567dca99500ca6f /report | |
parent | c574a2da39d3c4f7fb474f75bd1660fd4380dd1c (diff) | |
parent | 58c7f95f9bc940a88ffe6fc3ccd19e1417f77062 (diff) | |
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Merge branch 'master' of skozl.com:e4-pattern
Diffstat (limited to 'report')
-rwxr-xr-x | report/paper.md | 4 |
1 files changed, 2 insertions, 2 deletions
diff --git a/report/paper.md b/report/paper.md index d3e426f..79d4183 100755 --- a/report/paper.md +++ b/report/paper.md @@ -49,7 +49,7 @@ be used as an alternative to euclidiean distance. To evaluate improvements brought by alternative distance learning metrics a baseline is established through nearest neighbour identification as previously described. Identification accuracies at top1, top5 and top10 are respectively 47%, 67% and 75% -(figure \ref{fig:baselineacc}). The mAP is 47%. +(figure \ref{fig:baselineacc}). The mAP is 47.2%. \begin{figure} \begin{center} @@ -215,7 +215,7 @@ It is also necessary to estimate how precise the ranklist generated is. For this reason an additional method of evaluation is introduced: mAP. See reference @mAP. It is possible to see in figure \ref{fig:ranklist2} how the ranklist generated for the same five queries of figure \ref{fig:eucrank} -has improved for the fifth query. The mAP improves from 47% to 61.7%. +has improved for the fifth query. The mAP improves from 47.2% to 61.7%. \begin{figure} \begin{center} |