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| author | nunzip <np.scarh@gmail.com> | 2019-02-15 17:41:16 +0000 |
|---|---|---|
| committer | nunzip <np.scarh@gmail.com> | 2019-02-15 17:41:16 +0000 |
| commit | d2f8f7376a4a785f11f062dfd81ba83b9fb83cd3 (patch) | |
| tree | 6ca84fe359055ab8b1051488a4fc6b68a6d482cd | |
| parent | e3e713a66b0a1e85714d764663823c92ffbd1f2d (diff) | |
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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 |