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Table 1: Candidate generation operators Type II context sets (candidate evaluation) are assembled from evaluation metrics that can be used to compare two candidates. Context elements that de ne the conditions un- der which the metrics are meaningful are collected into context sets for each label in the 17
1991
"... In PAGE 17: ... Fac- tors that in uenced the choice of which operators to include were the likelihood of success, the ease of implementation, the lack of any alternative operators, and the availability of existing code. Table1 lists the types of operators that are actually employed by Condor to generate candidates (for the experimentation site in the foothills near Stanford University). For each operator, the assumptions that it requires are encoded as context elements in a context set that controls the invocation of the operator.... ..."
Cited by 53
Table 2. Some bisimilar states
2004
"... In PAGE 9: ... There are 28 states, s1 to s28,thusitisnot possible in Figure 7 to give meaningful labels. In Table2 we enumerate a few of the states. We give the name from the reagent-centric model first, followed by the name of the equivalent state from the pathway-centric model.... ..."
Cited by 30
Table 9. The completeness, conciseness, and consistency of the clusters created with our algorithm on the large dataset as compared to various AV vendors
2007
"... In PAGE 13: ... In particular, we demonstrate the completeness, conciseness, and consistency of the generated clusters. Our analysis of these properties, summarized in Table9 , are highlighted each in turn: Completeness. To measure completeness, we examined the number of times we created a meaningful label for a binary and compared this to the detection... ..."
Cited by 4
Table 2. Some bisimilar states
in algebra PEPA
"... In PAGE 8: ... There are 28 states, s1 to s28,thusitisnot possible in Figure 7 to give meaningful labels. In Table2 we enumerate a few of the states. We give the name from the reagent-centric model first, followed by the name of the equivalent state from the pathway-centric model.... ..."
Table 7 Necessary system knowledge for each of the 24 individualization cate gories.
1991
"... In PAGE 28: ... The necessary knowledge for user-tailoring a dialog system can be analyzed in more detail using the matrix classification scheme. In Table7 the necessary system knowledge is listed for each of the 24 individualization categories. Table 7 shows that method M1 (selectable alternatives) requires no special know- ledge about indivdualization procedures and is therefore most suitable for casual users, as has already been claimed.... In PAGE 28: ... In Table 7 the necessary system knowledge is listed for each of the 24 individualization categories. Table7 shows that method M1 (selectable alternatives) requires no special know- ledge about indivdualization procedures and is therefore most suitable for casual users, as has already been claimed. More interesting is the distinction between method M3 and M4 (configuration program/configuration file), since these methods offer the same possibilities (a high degree of freedom), but require different know- ledge.... In PAGE 28: ... More interesting is the distinction between method M3 and M4 (configuration program/configuration file), since these methods offer the same possibilities (a high degree of freedom), but require different know- ledge. Table7 shows that a special syntax in addition to the use of a text editor must be known for the configuration file method M4. Method M3 requires only knowledge about a special configuration program and will therefore be easier to understand.... In PAGE 29: ... Here user-tailoring becomes a dominant part of the user apos;s job and will be carried out regularly. In this case (see Table7 ) the... ..."
Cited by 16
Table 1: Meaningful Engagement Matrix
Table 4 shows some obtained meaningful bi-
1999
Cited by 2
Table 5.1: Create a group distribution of data: an example is a necessary preprocess for any interesting and meaningful analysis. In some cases, the generalization of the data is not very appealing in terms of mining knowledge. Nevertheless, generalization of the predictors can increase the computational e ciency substantially and decrease the e ect of noise data. Whereas category labels for nominal attributes are quite explicit (as in Table 5.1B, for example), those for numerical attributes need further explanation. Categories for the age distribution below Table 5.2A, for example, are expressed in terms of a unit of measurement, in this case, \1-year quot; units. While grouping is frequently helpful to summarize information, it also may pose problems, depending upon how the scores are distributed within categories and upon the number of categories that
Table 1 - Cluster name generated using two labeling methods.
"... In PAGE 4: ... The extractive multi-document summaries for the clusters were not always meaningful. They also did not always reflect the main topic of the citations in the cluster, for example the first cluster in Table1 was summarized by the first author as follows: Systemic diseases and conditions that predispose an individual to periodontal destruction are described. One of the papers discusses differential diagnosis of gingivitis in children.... ..."
Cited by 8
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