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There was no significant difference between different pathway categories or between different feature vectors.
This difference appears to be the result of using different feature importance measures.
The 30 rooms, all different, feature wooden floors, Oriental carpets, exposed beams.
Each color represents a different feature category.
Different feature selection algorithms are applied to improve detection accuracy.
These methods ignore the relation between different feature types.
Semantic roles/relations may categorize into different feature dimensions.
Table 4 shows the classifier accuracy for different feature combinations.
A different feature of cluster character is building usage.
Figure 4 Performance on different feature sets (soft case).
Figure 8 Performance evaluation for different feature subset sizes.
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