aboutsummaryrefslogtreecommitdiff
diff options
context:
space:
mode:
authornunzip <np.scarh@gmail.com>2019-02-15 17:41:16 +0000
committernunzip <np.scarh@gmail.com>2019-02-15 17:41:16 +0000
commitd2f8f7376a4a785f11f062dfd81ba83b9fb83cd3 (patch)
tree6ca84fe359055ab8b1051488a4fc6b68a6d482cd
parente3e713a66b0a1e85714d764663823c92ffbd1f2d (diff)
downloade4-vision-d2f8f7376a4a785f11f062dfd81ba83b9fb83cd3.tar.gz
e4-vision-d2f8f7376a4a785f11f062dfd81ba83b9fb83cd3.tar.bz2
e4-vision-d2f8f7376a4a785f11f062dfd81ba83b9fb83cd3.zip
Rewrite comparison
-rw-r--r--report/paper.md2
1 files changed, 1 insertions, 1 deletions
diff --git a/report/paper.md b/report/paper.md
index 885f27d..36259c6 100644
--- a/report/paper.md
+++ b/report/paper.md
@@ -148,7 +148,7 @@ In many applications the increase in training time would not justify the small i
For the Caltech_101 dataset, a RF codebook seems to be the most suitable method to perform RF classification.
-The `water_lilly` is the most misclassified class, both for K-means and RF codebook (refer to figures \ref{fig:km_cm} and \ref{fig:p3_cm}). This indicates that the features obtained from the class do not provide for very discriminative splits, resulting in the prioritsation of other features in the first nodes of the decision trees.
+The `water_lilly` is the most misclassified class, both for K-means and RF codebook (refer to figures \ref{fig:km_cm} and \ref{fig:p3_cm}). This indicates that the quantised descriptors obtained from the class do not provide for very discriminative splits, resulting in the prioritsation of other features in the first nodes of the decision trees.
# References