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| author | nunzip <np.scarh@gmail.com> | 2018-12-14 12:35:37 +0000 |
|---|---|---|
| committer | nunzip <np.scarh@gmail.com> | 2018-12-14 12:35:37 +0000 |
| commit | 8dad008167c2a7d9402947f6e5234ca752b7b099 (patch) | |
| tree | 9bb8406d452292b4dfb31abcef54214ca0831d84 /report/metadata.yaml | |
| parent | 1a67e68bd1b51b7a0ef860542a5ab488384d91b4 (diff) | |
| download | vz215_np1915-master.tar.gz vz215_np1915-master.tar.bz2 vz215_np1915-master.zip | |
Diffstat (limited to 'report/metadata.yaml')
| -rw-r--r-- | report/metadata.yaml | 2 |
1 files changed, 1 insertions, 1 deletions
diff --git a/report/metadata.yaml b/report/metadata.yaml index a5929fa..74450b7 100644 --- a/report/metadata.yaml +++ b/report/metadata.yaml @@ -13,7 +13,7 @@ nocite: | abstract: | This report analyses distance metrics learning techniques with regards to identification accuracy for the dataset CUHK03. The baseline method used for - identification is Nearest Neighbors based on Euclidean distance. + identification is Nearest Neighbours based on Euclidean distance. The improved approach evaluated utilises Jaccardian metrics to rearrange the NN ranklist based on reciprocal neighbours. While this approach is more complex and introduces new hyperparameters, significant accuracy improvements are observed - |