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TABLE 6 Advantages and Disadvantages of Oracle Generation

in Using Test Oracles Generated from Program Documentation
by Dennis Peters, Student Member, Ieee David, Lorge Parnas, Senior Member 1998
Cited by 40

Table 3: Advantages and Disadvantages of ER/EER and OO/OMT Models: ER/EER Model OO/OMT Model

in Users' comprehension of ternary relationships . . . Relationship Model and Object Modeling Technique
by Zhihui Liu 2000
"... In PAGE 5: ...able 2: Basic Correspondence between EER and OMT ................................................. 17 Table3 : Advantages and Disadvantages of ER/EER and OO/OMT Models:.... In PAGE 16: ... It promotes database integrity and integration, for the object paradigm helps to bridge the semantic gap between databases and applications (Blaha, Premerlani, Rumbaugh, 1988, 425). Table2 and Table3 on the following two pages summarize the comparative studies on model coverage, model connectivity, model advantages and disadvantages between the ER/EER and the OO/OMT models (Elmasri and Navathe, 1999; Blaha and Premerlani, 1998; Blaha et al., 1988; Navathe, 1992; Sanders, 1995; Gray et al.... ..."

TABLE I Error percentages of the two classi ers and of both. If we have an oracle to choose which classi er to use, error on WI can be reduced to 1.70%. Set

in Techniques for Combining Multiple Learners
by Ethem Alpaydin 1998
Cited by 8

Table (Oracle

in unknown title
by unknown authors 2004
Cited by 1

Table Oracle

in P*TIME: Highly Scalable OLTP DBMS for Managing Update-Intensive Stream Workload
by Sang K. Cha, Changbin Song 2004
Cited by 1

Table VI. Synopsis of Model-Driven Generators (Oracle Designer 2000 and Hyperwave) Designer 2000 Hyperwave Process: Lifecycle Coverage Conceptualization (E/R) Conceptualization (collection and link definition)

in Tools and Approaches for Developing Data-Intensive Web Applications: A Survey
by Piero Fraternali, Politecnico Di Milano 1999
Cited by 89

Table 2. Frequency of Deleterious, Neutral and Advantageous crossovers on RR.

in Maximum homologous crossover for linear genetic programming
by Michael Defoin Platel, Manuel Clergue 2003
"... In PAGE 8: ... Other crossover events are said selectively Neutral (noted N). Table2 reports the frequency of such crossover events. We observe, for N=8 and N=16, that D events prevail with SC and that their frequency is dramatically reduced using MHC.... ..."
Cited by 4

Table 2: Average Adoption Time and Advantage at the Time of Adoption, Baseline Case

in Strategic Experimentation and Disruptive Technological Change
by Fabiano Schivardi, Martin Schneider
"... In PAGE 29: ... This is due to the fact that, for lowvalues, the incumbent is likely to fall behind and #5Cgamble for resurrection, quot; while for high values the Type I mistake is more likely. Table2 reports the average number of periods before switching takes place and the average advantage at the time of switching for a subset of the cases of Table 1. The Table shows that the average delay in adoption decreases with d 0 , and that the average advantage at adoption increases.... In PAGE 30: ... Table 1 shows that the percentage of adoptions are uniformly lower for the p = :7 case. Table2 shows that adoption time increases dramatically for the p = :7 case. Less predictably, a comparison between the advantage at the time of adoption shows that this is lower for p = :7 case.... In PAGE 31: ...2 Replacement We now turn to the question of the persistence of market power and analyze who will be the dominant #0Crm at the end of the episode. Again the certainty game is a useful benchmark against 22 These two results are con#0Crmed by the comparison of the #0Crst with the third sub-column in Table2 for the adoption time and the second with the fourth sub-columns for the advantage at adoption.... In PAGE 35: ... Table 4 shows that the average adoption time is greatly increased by the drop, because the incumbent wants to collect more evidence before being willing to accept the drop in performance. In terms of advantage, we note that, taking the drop into account, the values are in general not below the corresponding ones in Table2 . This is because the incumbent switches only under particularly favorable circumstances for the performance advantage, otherwise he would rather #5Cgamble quot; and not adopt.... ..."

Table 3.4: Results of using the Hausdor nearest neighbor method on observed images. The accuracy was 94.6%. The results are dramatically better than for the independent pixel model, but still inferior to the factorized classi er using the Hausdor distance.

in Learning from One Example in Machine Vision by Sharing Probability Densities
by Erik G. Miller

Table 1: Accuracy/F1-score comparison: [m]=# labeled data. [oracle]= oracle performance when selecting the hypothesis with the lowest risk on the test data . [ER]= selection based on the empirical risk on the training data. [n-uni]= n-ary learner with uniform prior. [n-joint]=n-ary learner with estimated joint prior. [multi]=multi-label with estimated joint prior. Best results are in bold.

in Structured multi-label transductive learning: a case study . . .
by Kevin Duh, Katrin Kirchhoff
"... In PAGE 3: ... decision= A , then accuracy=0; precision=1, recall=0.5, F1-score=0.67). Table1 compares the performance of (1) n-ary with uniform prior [n-uni], (2) n-ary with estimated joint label priors [n-joint], and (3) multi-label with joint priors [multi]. The col- umn [oracle] shows the best possible accuracy that can be achieved among all proposed hypotheses; the column [ER] reports the accuracy for the case where the hypothesis selec- tion is based on the minimum empirical risk on the labeled data.... ..."
Cited by 1
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