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Table 4.7: Whether respondents asked explicit questions regarding their athletic ability in the additional data collections.
2005
Table 1: Performance of the SAIL robot in grounded speech learning. After training, the trainer tested the SAIL robot by guiding it through the second floor of Engineering Building. As SAIL did not have perfect heading alignment, the human trainer used verbal commands to adjust robot heading during turns and straight navigation. During the navigation, the arm and eye commands are issued 10 times each at different locations. Commands Go left Go right Forward Backward Freeze
2002
"... In PAGE 8: ... Typically, after about 15-30-minute interactions with a particular human trainer, the SAIL robot could follow commands with about 90% correct rate. Table1 shows the voice commands learned by the SAIL robot and its perfor- mance. Fig.... ..."
Cited by 6
Table 5: Athletics video: Classification matrix obtained when considering the residual motion only. Left: with the GMM model. Right: with the DGMM model.
2002
"... In PAGE 5: ...3 Experimental comparisons Table 4 contains results when considering the camera mo- tion only. Conversely, Table5 gives results obtained when... ..."
Cited by 1
Table 3.10: Whether respondents asked about their general attitudes toward (intercollegiate) athletics in data collections conducted by the INSTRUMENT institutions.
2005
Table 3.13: Whether respondents asked explicit questions regarding their athletic ability in data collections conducted by the INSTRUMENT institutions.
2005
Table 3.14: Whether respondents asked whether they had been recruited as athletes by some postsecondary institution in data collections conducted by the INSTRUMENT institutions.
2005
Table 3.15: Whether respondents were provided caveats and benefits concerning athletic participation in data collections conducted by the INSTRUMENT institutions.
2005
Table 5: Athletics video: Classification matrix obtained when considering the residual motion only. Left: with the GMM model. Right: with the DGMM model.
2003
"... In PAGE 71: ...3 Experimental comparisons Table 4 contains results when considering the camera mo- tion only. Conversely, Table5 gives results obtained when using the residual motion model only. These two tables... ..."
Table 2: Constraints on parameters for the lexical valence probability computation for different interpretations (DO and SC). The table above shows the lexical valence structures for the sentences The young athlete realized her potential...at two stages; one before and one after the NP her potential .
1998
"... In PAGE 23: ... As in the case of Bayes net model of syntax (SCFG), input coming in allows for the re-estimation of posterior probabilities for the different interpretations. Table 1 and Table2 show examples of parameters encoded in the lexical Bayes net in Figure 13. Table 1 pertains specifically to the network in Figure 13.... In PAGE 25: ...ickering et al. (2000) (recall The athelete realized her ... examples from the previous section and from the in- troduction). Table2 shows the parameters for the different interpretations (Sentential Complement (SC) and Direct Object (DO)) after the input the young athlete realized her potential . Details of the network structure and model can... ..."
Cited by 20
Table 4: Athletics video: Classification matrix obtained when considering the camera motion only. Left: on the segments se- lected after the first step based on the GMM model. Right: on the segments selected after the first step based on the DGMM model. Assigned label
2002
"... In PAGE 5: ... 6.3 Experimental comparisons Table4 contains results when considering the camera mo- tion only. Conversely, Table 5 gives results obtained when... ..."
Cited by 1
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