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authorVasil Zlatanov <v@skozl.com>2018-12-13 14:37:28 +0000
committerVasil Zlatanov <v@skozl.com>2018-12-13 14:37:28 +0000
commit593e701b22513809816359f14a5045528cc50bef (patch)
treecfc7b00f730bc9f93d00a321c567dca99500ca6f
parentc574a2da39d3c4f7fb474f75bd1660fd4380dd1c (diff)
parent58c7f95f9bc940a88ffe6fc3ccd19e1417f77062 (diff)
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Merge branch 'master' of skozl.com:e4-pattern
-rwxr-xr-xreport/paper.md4
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}