### Table 1: Sample Space Listing (Housing Component)

2000

### Table 2. The results achieved under different sampling spaces.

"... In PAGE 5: ... In order to verify our conjecture, we conducted two groups of experiments. Table2 shows the results using dif- ferent sampling spaces denoted by M. Here, multi-class SVM is used as a classifier.... ..."

### Table 1 - Sampling of STOW Routing Spaces

1997

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### Table 1. Sample attributes for feature space and their encoding.

### Table 1: Inversion precision where the number of samples of the null space is 256

2001

"... In PAGE 3: ... We can thus consider that this example gives a good idea about the precision. We present the results in Table1 and Table 2 for two different sampling steps: 1. the number of samples of the null space is BEBHBI 2.... ..."

Cited by 2

### Table 1. The space density of clusters Sample

"... In PAGE 3: ... We have estimated the mean space density of clusters in this sample, using Equation 3 of Efstathiou et al. (1992) and the results are given in Table1 . We have also applied suc- cessively higher richness bounds to create subsamples with lower space densities, the estimated space densities as listed in Table 1.... In PAGE 3: ... (1992) and the results are given in Table 1. We have also applied suc- cessively higher richness bounds to create subsamples with lower space densities, the estimated space densities as listed in Table1 . We also list the space density of sample B and a subsample with R 70.... In PAGE 3: ... We also list the space density of sample B and a subsample with R 70. 3 CLUSTER CORRELATIONS We estimate the redshift-space correlation functions for the samples in Table1 by cross-correlating with a random cat- alogue and using the estimator cc(s) = 2f DD DR ? 1; (3) where DD and DR are the number of cluster-cluster pairs and the number of cluster-random pairs respectively in each bin centred on s. The parameter f is the ratio of the number of random points to the number of clusters in the sample.... In PAGE 7: ... If the error bars are taken at face value, then the relation would appear to be ruled out at the 2 level. However, as we have seen from Table1 , the error bars could be underestimates by a factor of 1:1?2:1. Also, the space densities of clusters used to derive dc values are not precise estimates because of the di culties involved in estimating the completeness of richness limited cluster cat- alogues (see Efstathiou et al.... ..."

### Table 2.1: Table of notation Z Space of samples

2006

### Table 1: Improvements achievable by input space reduction and sampling without replacement

"... In PAGE 6: ...Table 1: Improvements achievable by input space reduction and sampling without replacement Table1 illustrates the improvements in the probability of de- tecting a failure by sampling without replacement in the re- duced input space as a function of the factor (t ? 1) R. The results in the second and third column of the table are obtained by calculating P from Formula 4.... In PAGE 6: ... The quo- tient t R N can be understood as the sampling fraction of the input domain. In the first row of Table1 , P is obtained from the original input space. Samplings with and without replacement do not differ unless the test cases cover at least 0.... ..."

### Table 1: Classification of the sample applications according to the design space.

"... In PAGE 4: ... Therefore, we have decided to base our discussion on these concrete examples rather than speculating about the inherent characteristics of a cer- tain type of application. Table1 classifies the sample ap- plications according to the dimensions of the design space described in the previous section. 3.... ..."

### Table 3 Importance sampling applied to multidimensional state space, , , , ,

1997

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