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Table 9. The difference between maximum and minimum belief and error impact confusion.
"... In PAGE 8: ... In interpreting the information in Table 8, it seems reasonable to take the confusion level differences into consideration. We have merged the contents of Table 8 with these differences and presented the result in Table9 for a convenient overview. ... In PAGE 9: ...style and image, but the figures in Table9 seem to suggest that the weight is more prominently on image. Table 9.... ..."
Table 2: Case of Impossible Partition
"... In PAGE 6: ... There are some cases that a data set can not be further partitioned. For example in the training set of Table2 , it is impossible for a classifier to split the first two records into two separate data sets since there all have identical attributes except the classifying attribute Lived. There is no classification rule that can explain why the two records have different class labels.... ..."
Table 2. 4dH; Theorem 10. Credulous and skeptical CHIM operators satisfy (IC0)-(IC3), (IC7) and (IC8). Thus, some important properties of IC merging operators are usually lost through the merge-then-revise process. We claim that this is not so dramatic since the main pur- pose of conciliation processes is not exactly the one of belief merging. Furthermore, speci c iterated merging operators (i.e., those induced by some speci c merging oper- ators 4) may easily satisfy additional postulates: Theorem 11. The credulous iterated merging operator associated to 4dD;Max
2005
"... In PAGE 9: ... Let us consider the credulous CHIMC operator de ned from the merging operator 4dH; . The computations are summarized in Table2 . The resulting pro le is [K2 1] = f(0; 0; 1)g, [K2 2] = f(1; 0; 0)g, [K2 3] = f(0; 0; 1)g and [K2 4] = f(1; 0; 0)g.... ..."
Cited by 4
Table 3: Results with merged trees.
1986
"... In PAGE 10: ... For each run, C1, C2, and C3 were found, merged into a single tree C1:2:3, and this tree further reduced to C0 1:2:3 by the removal of unpopulated leaves. Only 24 of the previous 27 datasets could be processed in this way { three of the largest gave rise to merged trees so huge that virtual memory was exhausted! Results for these 24 datasets appear in Table3 . The rst part shows the sizes of these trees as measured by their numbers of leaves.... In PAGE 10: ... The great majority of leaves on C1:2:3 have no corresponding training cases because, when these unpopulated leaves are removed, the resulting tree C0 1:2:3 is much smaller; on average, it is just under four times as big as C1. The second section of Table3 concerns error rates of the merged trees when used to classify unseen cases, expressed as percentages and ratios to the error rates of the single tree C1. Overall, as expected, C1:2:3 performs very similarly to the boosted classi er CB { the geometric mean of the ratios for these datasets, .... ..."
Cited by 2605
Table 3: Results with merged trees.
1986
"... In PAGE 10: ... For each run, C1, C2, and C3 were found, merged into a single tree C1:2:3, and this tree further reduced to C0 1:2:3 by the removal of unpopulated leaves. Only 24 of the previous 27 datasets could be processed in this way { three of the largest gave rise to merged trees so huge that virtual memory was exhausted! Results for these 24 datasets appear in Table3 . The rst part shows the sizes of these trees as measured by their numbers of leaves.... In PAGE 10: ... The great majority of leaves on C1:2:3 have no corresponding training cases because, when these unpopulated leaves are removed, the resulting tree C0 1:2:3 is much smaller; on average, it is just under four times as big as C1. The second section of Table3 concerns error rates of the merged trees when used to classify unseen cases, expressed as percentages and ratios to the error rates of the single tree C1. Overall, as expected, C1:2:3 performs very similarly to the boosted classi er CB { the geometric mean of the ratios for these datasets, .... ..."
Cited by 2605
Table 2. Impossible Di erentials of Khufu and Khafre
1999
Cited by 4
Table 2 Number of Beliefs for Which Beliefs Scores Changed, by Group Changed on at
"... In PAGE 11: ... Another way that we compared the beliefs-survey change scores was to examine the number of beliefs that that showed score change for each individual. Table2 shows that the majority of the control group and the MORE-R group (58.82% and 52.... ..."
Table 1. Data and beliefs: an overview
2004
"... In PAGE 5: ...The basic distinction between data and beliefs yields a rich picture of epistemic dynamics (Fig. 1 and Table1 ). From a computational viewpoint, such distinction opens the way for blended approaches to implementation [20]: data structures present remarkable similarities with Bayesian networks and neural networks, while belief sets are a well-known hallmark of AGM-style belief revision [13].... ..."
Cited by 4
Table 1: Description of the belief functions
"... In PAGE 12: ... The ignorance is not maintained and all cases are treated. Table1 below presents all belief functions used in the calculation of extremities. For a frame of discernment we give the calculation for every focal element and for the ignorance if it is managed.... ..."
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