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| author | nunzip <np.scarh@gmail.com> | 2018-12-11 13:06:07 +0000 |
|---|---|---|
| committer | nunzip <np.scarh@gmail.com> | 2018-12-11 13:06:07 +0000 |
| commit | 395612fa3780e8addebe63544c9a050a851ee575 (patch) | |
| tree | a8c1e9ed3ef154001a336ab9681ad54abfbc3b07 /report2/paper.md | |
| parent | 7e95ffdec0b743ea2466f85323440a448bd7e2fe (diff) | |
| download | vz215_np1915-395612fa3780e8addebe63544c9a050a851ee575.tar.gz vz215_np1915-395612fa3780e8addebe63544c9a050a851ee575.tar.bz2 vz215_np1915-395612fa3780e8addebe63544c9a050a851ee575.zip | |
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| -rwxr-xr-x | report2/paper.md | 2 |
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diff --git a/report2/paper.md b/report2/paper.md index 98915e8..68b803a 100755 --- a/report2/paper.md +++ b/report2/paper.md @@ -81,7 +81,7 @@ This is due to the fact that the feature vectors appear scaled, releative to the significance, for optimal distance classification, and as such normalising loses this scaling by importance which has previously been introduced to the features. -## kMean Clustering +## kMeans Clustering An addition considered for the baseline is *kMeans clustering*. In theory this method allows to reduce computational complexity of the baseline NN by forming clusters and |