From 646a0e43c4a52d16528c9d2692143e90b56e8774 Mon Sep 17 00:00:00 2001 From: Vasil Zlatanov Date: Tue, 20 Nov 2018 18:40:43 +0000 Subject: Add conclusion --- report/paper.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/report/paper.md b/report/paper.md index 0f385c1..af24be3 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 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. + # References
-- cgit v1.2.3-54-g00ecf From 9b2c9ecd9d492f6368fd600a497719813348365e Mon Sep 17 00:00:00 2001 From: Vasil Zlatanov Date: Tue, 20 Nov 2018 18:40:49 +0000 Subject: Remove location --- report/metadata.yaml | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/report/metadata.yaml b/report/metadata.yaml index 97c0c24..5c4dde1 100755 --- a/report/metadata.yaml +++ b/report/metadata.yaml @@ -2,9 +2,7 @@ title: 'EE4-68 Pattern Recognition (2018-2019) CW1' author: - name: Vasil Zlatanov (01120518), Nunzio Pucci (01113180) - affilation: Imperial College - location: London, UK - email: vz215@ic.ac.uk, np1915@ic.ac.uk + location: vz215@ic.ac.uk, np1915@ic.ac.uk numbersections: yes lang: en babel-lang: english -- cgit v1.2.3-54-g00ecf From 83ad9d43910641e5eb37bd488afc6375c12a9f32 Mon Sep 17 00:00:00 2001 From: Vasil Zlatanov Date: Tue, 20 Nov 2018 18:43:19 +0000 Subject: Fix grammer in conclusion --- report/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) 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 -- cgit v1.2.3-54-g00ecf