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| author | nunzip <np.scarh@gmail.com> | 2018-11-20 18:56:19 +0000 |
|---|---|---|
| committer | nunzip <np.scarh@gmail.com> | 2018-11-20 18:56:19 +0000 |
| commit | 1e0e0800a64c0adcf13021795fa064e86efd3f74 (patch) | |
| tree | 4352841edbc40578d8ea717cd3fe1f6a7c64aa2c /report/paper.md | |
| parent | 4e2519cf0246364621278f3f59d516f1c9b3d664 (diff) | |
| parent | 83ad9d43910641e5eb37bd488afc6375c12a9f32 (diff) | |
| download | vz215_np1915-1e0e0800a64c0adcf13021795fa064e86efd3f74.tar.gz vz215_np1915-1e0e0800a64c0adcf13021795fa064e86efd3f74.tar.bz2 vz215_np1915-1e0e0800a64c0adcf13021795fa064e86efd3f74.zip | |
Merge branch 'master' of skozl.com:e4-pattern
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| -rwxr-xr-x | report/paper.md | 4 |
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diff --git a/report/paper.md b/report/paper.md index aa998b4..5c145c2 100755 --- a/report/paper.md +++ b/report/paper.md @@ -461,6 +461,10 @@ Seed & Individual$(M=120)$ & Bag + Feature Ens.$(M=60+95)$\\ \hline \label{tab:compare} \end{table} +# 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 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 <div id="refs"></div> |