### Table 7: A taxonomy of memory allocation algorithms discussed in this paper.

"... In PAGE 11: ...2. Table7 presents a summary of the above allocator algorithms, along with their speed, scalability, false sharing and blowup char- acteristics. As can be seen from the table, the algorithms closest to Hoard are Vee and Hsu, DYNIX, and LKmalloc.... ..."

### Table 7: A taxonomy of memory allocation algorithms discussed in this paper.

"... In PAGE 11: ...2. Table7 presents a summary of the above allocator algorithms, along with their speed, scalability, false sharing and blowup char- acteristics. As can be seen from the table, the algorithms closest to Hoard are Vee and Hsu, DYNIX, and LKmalloc.... ..."

### Table 1. Categorization of various transient detectors discussed in this paper.

### Table 2. Overview of the biomedical applications of information fusion discussed in this paper

"... In PAGE 9: ... In four common biomedical image analysis tasks, we have illustrated that problems can often be approached by algorithms operating in either of these domains, with specific advantages and disadvantages. Table2 gives a brief summary of our examples and the respective COI and COD methods applied to them. 1 The accuracy of a volumetric inter-individual nonrigid transformation between two different subjects is not a particularly well-defined concept.... ..."

### Table 1 Theories, systems and models discussed in this paper Classical decision theory

2002

"... In PAGE 3: ... However, this comparison gives some interesting insights into the relation among the areas, and these insights are a good starting point for further and more complete comparisons. A summary of the comparison is given in Table1 . In our comparison, some concepts can be mapped easily onto concepts of other theories and systems.... ..."

### Table 1: A taxonomy of memory allocation algorithms discussed in this paper.

"... In PAGE 3: ... Un- like Hoard, both of these allocators passively induce false sharing by allowing pieces of the same cache line to be recycled. Table1 presents a summary of allocator algorithms, along with their speed, scalability, false sharing and blowup characteristics. Hoard is the only one that solves all four problems.... ..."

### Table 1. Summary and comparison of integration solutions discussed in this paper

"... In PAGE 8: ... 4. Tool bus integration with integrated front-ends Table1 summarizes the alternative tool interoperability solutions, in terms of the aspects of integration addressed and the level of flexibility of the solution. Table 1.... ..."

### Table 2. Experimental results of the primary approaches discussed in this paper. Dataset NB BN

1997

"... In PAGE 19: ... The latter approach searches for the subset of attributes over which naive Bayes has the best performance. The results, displayed in Figures 5 and 6 and in Table2 , show that TAN is quite competitive with both approaches and can lead to signi cant improvements in many cases. 4.... In PAGE 25: ... In particular, the cross- validation folds were the same for all the experiments on each dataset. Table2 displays the accuracies of the main classi cation approaches we discussed throughout the paper: NB: the naive Bayesian classi er BN: unrestricted Bayesian networks learned with the MDL score TANs: TAN networks learned according to Theorem 2, with smoothed parameters C+Ls: Chow and Liu method|Bayesian multinets learned according to Theo- rem 1|with smoothed parameters C4.5: the decision-tree classi er of (Quinlan, 1993) SNB: the selective naive Bayesian classi er, a wrapper-based feature selection ap- plied to naive Bayes, using the implementationof John, Kohavi, and P eger (1994) In the previous sections we discussed these results in some detail.... In PAGE 25: ... We now sum- marize the highlights. The results displayed in Table2 show that although unre- stricted Bayesian networks can often lead to signi cant improvement over the naive Bayesian classi er, they can also result in poor classi ers in the presence of multi- ple attributes. These results also show that both TAN as well as Chow apos;s and Liu apos;s classi ers are (1) roughly equivalent in terms of accuracy, (2) dominate the naive Bayesian classi er, and compare favorably with both C4.... ..."

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