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| author | Vasil Zlatanov <v@skozl.com> | 2018-11-20 18:43:19 +0000 |
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| committer | Vasil Zlatanov <v@skozl.com> | 2018-11-20 18:43:19 +0000 |
| commit | 83ad9d43910641e5eb37bd488afc6375c12a9f32 (patch) | |
| tree | a454ab5e5dfd79fe5214c8025beca557eaf82c1b /report/paper.md | |
| parent | 9b2c9ecd9d492f6368fd600a497719813348365e (diff) | |
| download | vz215_np1915-83ad9d43910641e5eb37bd488afc6375c12a9f32.tar.gz vz215_np1915-83ad9d43910641e5eb37bd488afc6375c12a9f32.tar.bz2 vz215_np1915-83ad9d43910641e5eb37bd488afc6375c12a9f32.zip | |
Fix grammer in conclusion
Diffstat (limited to 'report/paper.md')
| -rwxr-xr-x | report/paper.md | 2 |
1 files changed, 1 insertions, 1 deletions
diff --git a/report/paper.md b/report/paper.md index af24be3..523e6a1 100755 --- a/report/paper.md +++ b/report/paper.md @@ -463,7 +463,7 @@ Seed & Individual$(M=120)$ & Bag + Feature Ens.$(M=60+95)$\\ \hline # Conclusion -We have looked at the relevance of PCA and LDA when applied to face recognition, and analyzed the individual and combined performance. We have further looked at improvement made available by ensemble learning, utilising data and feature randomisation together with PCA-LDA and found that it is an effective approach to face recognition. +We have looked at the relevance of PCA and LDA when applied to face recognition, and analyzed the individual and combined performance. We have further looked at improvements made available by ensemble learning, utilising data and feature randomisation together with PCA-LDA and found it to be an effective approach to face recognition. # References |