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Data Analysis Machine Learning and Applications Episode 2 Part 2

Tham khảo tài liệu 'data analysis machine learning and applications episode 2 part 2', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | Collective Classification in 2D and 3D Range Data 297 2L JK X 2 C f FDRf T 6 uz j i j i where the subscripts i j refer to the mean and variance of the classes wi and Wj respectively. Additionally the cross-correlation coefficient between any two features f and g given T training examples is defined as n St 1 XtfXtg p fg I --------- . 7 ựzT 1 Xf ET 1 x2g where xtf denotes the value of the feature f in the training example t. Finally the selection of the best L features involves the following steps Select the first feature f1 as f1 argmaXf C f . Select the second feature f2 as f2 argmax a.1C f - Ư.2 pf1 f I f Ì f1 where a1 and a2 are weighting factors. Select fl l 1 . L such that _ I 1 1C f - I 112 Ip frf J r 1 2 . I -1 6 Experiments The approach described above has been implemented and tested in several 2D maps and 3D scenes. The goal of the experiment is to show the effectiveness of the iAMN in different indoor range data. 6.1 Classification of places in 2D maps This experiment was carried out using the occupancy grid map of the building 79 at the University of Freiburg. For efficiency reasons we used a grid resolution of 20cm which lead us to a graph of 8088 nodes. The map was divided into two parts the left one used for learning and the right one used for classification purposes Figure 1 . For each cell we calculate 203 geometrical features. This number was reduced to 30 applying the feature selection of Section 5. The right image of Figure 1 shows the resulting classification with a success rate of 97.6 . 298 Triebel et al. Corridor Room Doorway Fig. 1. The left image depicts the training map of building 79 at the University of Freiburg. The right image shows the resulting classified map using an iAMN with 30 selected features. 6.2 Classification of objects in 3D scenes In this experiment we classify 3D scans of objects that appear in a laboratory of the building 79 of the University of Freiburg. The laboratory contain tables chairs monitors and ventilators. For