### Table 1. Performance Characteristics of Different AM Implementations

1997

"... In PAGE 7: ...ficient, buffered writes in the SCI DSM only. Performance measurements on the UCSB SCI cluster show competitive performance behavior of the SCI AM system ( Table1 ). Our own implementation, depicted in the first row of Table 1, adds little over- head to the raw latency of 9.... ..."

Cited by 13

### TABLE 2. Steps for computing optimal partitions with Rmax D 4

1997

Cited by 2

### Table 4. Optimal intron linear discrimination function

1998

Cited by 1

### Table 2: Discrimination results in %.

"... In PAGE 10: ... In the experiments, the number of clusters is fixed at two (binary sense discrimination). Table2 gives the discrimination results for the pseudowords considered. The first two measures (S1, S2) gives the percentage of correct senses for each of the two words making the pseudoword.... ..."

### Table 2: Cluster discrimination measure (CDM) for height and relative height.

"... In PAGE 4: ... The data was unit-scaled prior to applying the principal component analysis (PCA). height are shown in Table2 . Between the two features there is only a small difference; therefore we do not expect the choice between them to have much in uence on our results.... ..."

### Table 10: Number of optimal discriminators for each training set.

2005

"... In PAGE 8: ...ion 4.4. First, FCBF algorithm ranked the discriminators in order of importance and then by performing experiments on an indepen- dent dataset, we were able to identify the optimal number of dis- criminators. Table10 demonstrates those numbers for each training set. It could be seen immediately that the aim of FCBF ltering has been ful lled.... In PAGE 9: ...5 Nacurrency1 ve Bayes, kernel density estimation technique after FCBF pre ltering In this section we are considering the most advanced tool in this paper, Nacurrency1 ve Bayes with kernel density estimation after FCBF pre- ltering. The number of discriminators chosen for the analysis of each dataset is demonstrated in Table10 . These numbers were ob- tained as described in the section 4.... ..."

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### Table 7: Sparse SDPs from combinatorial optimization name m n

2005

"... In PAGE 21: ... 4.2 Numerical results on the SDPARA-C In Table7 , we apply the SDPARA-C to three SDPs, which arise from combinatorial op- timization. They are SDP relaxations of the maximum cut problems and the max clique number problems on lattice graphs, respectively.... ..."

### Table II. Comparison of preconditioners for sparse optimal control problems.

### Table 1. Optimal donor site linear discriminant func- tion

1998

Cited by 1