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| author | Vasil Zlatanov <v@skozl.com> | 2018-11-20 15:01:03 +0000 |
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| committer | Vasil Zlatanov <v@skozl.com> | 2018-11-20 15:01:03 +0000 |
| commit | 042f56da4b930f8ed15e763011b7b19cb3fd517f (patch) | |
| tree | 44c6fc40f0c08fdfd7590e8ca789794d2fda2cba | |
| parent | 68bbd7cc485653f947d24bca63fb41cf0b720d4b (diff) | |
| download | vz215_np1915-042f56da4b930f8ed15e763011b7b19cb3fd517f.tar.gz vz215_np1915-042f56da4b930f8ed15e763011b7b19cb3fd517f.tar.bz2 vz215_np1915-042f56da4b930f8ed15e763011b7b19cb3fd517f.zip | |
Force table position
| -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 b2c4243..f5b1934 100755 --- a/report/paper.md +++ b/report/paper.md @@ -459,7 +459,7 @@ We can compute an ensemble confusion matrix before the committee machines as sho Combining bagging and feature space randomization we are able to consistently achieve higher test accuracy than the individual models. In table \ref{tab:compare} $70/30$ splits. -\begin{table}[] +\begin{table}[ht] \begin{tabular}{lrr} \hline Seed & Individual$(M=120)$ & Bag + Feature Ens.$(M=60+95)$\\ \hline 0 & 0.916 & 0.923 \\ |