### Table 2 Comparison of Zernike moments computed in continuous and discrete domains

2006

"... In PAGE 9: ...0. The computed Zernike moments using each method are re- ported in Table2 . The explicit form of the radial polynomi- als in Table 2 is referred from [15].... In PAGE 9: ...rogram implemented with MicrosoftTM visual C ++6.0. The computed Zernike moments using each method are re- ported in Table 2. The explicit form of the radial polynomi- als in Table2 is referred from [15]. Note that the two sets of Zernike moments are almost the same.... ..."

### Table 1: description of the meshes used to discretize the domain with a hole of radius quot;.

2005

"... In PAGE 4: ... 1). In Table1 we show the mesh used to analyze the problem with hole. Table 1: description of the meshes used to discretize the domain with a hole of radius quot;.... ..."

### Table 2: Representation of Data Type { DATA WITH ANALOGY OVER DISCRETE DOMAIN This group of data is 5

### Table 2. Comparison of majority, C4.5, IDTM, and IDTM on non-discrete domains. Bold indicates signi cantly better accuracies (either C4.5 or IDTM.)

1995

"... In PAGE 10: ...5% accuracy. Table2 shows some results for datasets containing continuous features. In these domains, we expected IDTM to fail miserably, given that the chances of matching continuous features in the table are slim without preprocessing the data.... ..."

Cited by 63

### Table I. Details of experimental domains. C = continuous. D = discrete. domain number of instances number of classes number of features and their types Ionosphere 351 2 34 (34-C)

in Feature Subset Selection by Bayesian networks: a comparison with genetic and sequential algorithms

### Table 10: Complexity of computing the minimal domains in tractable augmented qualitative networks. discrete domains, we shall keep two pointers, Inf and Sup, to inf(Di) = v1 and sup(Di) = vk, respectively. We shall use three parameters in analyzing the computational complexity of algorithms: n|the number of nodes in the network, e|the number of arcs, and k|the maximum domain size, that is, the number of values in a domain (for discrete domains) or the number of intervals per domain (for continuous domains). In the rest of this section we show that for augmented CPA networks and for some augmented PA networks, the interesting tasks can be solved in polynomial time using local consistency algorithms such as arc consistency (AC) and path consistency (PC).

1991

Cited by 132

### Table 10: Complexity of computing the minimal domains in tractable augmented qualitative networks. discrete domains, we shall keep two pointers, Inf and Sup, to inf(Di) = v1 and sup(Di) = vk, respectively. We shall use three parameters in analyzing the computational complexity of algorithms: n|the number of nodes in the network, e|the number of arcs, and k|the maximum domain size, that is, the number of values in a domain (for discrete domains) or the number of intervals per domain (for continuous domains). In the rest of this section we show that for augmented CPA networks and for some augmented PA networks, the interesting tasks can be solved in polynomial time using local consistency algorithms such as arc consistency (AC) and path consistency (PC).

1991

Cited by 132

### Table 1. Description of the HIFF, IsoPeak, and IsoTorus fitness functions. The first column describes the objective funtion, the second the size of the individual, and the third and the fourth contain are the optimum solutions and their respective fitness values.

2004

"... In PAGE 11: ... We tried three standard optimization problems in the discrete domain such as HIFF, IsoPeak, and IsoTorus, which are known to be complex and full of local optima. Table1 de- scribes briefly these three functions. The reader can find more information on these problems in (Santana, 2004).... ..."

Cited by 2

### Table 1. E ciency of parallel algorithms

1998

"... In PAGE 17: ... The Schwarz alternating procedure with overlapping has been used. The e ciency of parallel iterative algorithms is reported in Table1 using the classical de nition of e ciency: e = t1 tp 1 p,wheretpdenotes the computing time using p processors. Results are given for discretized domains with 25000 points.... In PAGE 17: ... Results are given for discretized domains with 25000 points. From Table1 it can be seen that the e ciency of asynchronous iterations with order intervals is better than the e ciency of parallel synchronous iterations. Idle time due to synchro-... ..."

Cited by 10

### Table 2: Surface multiresolution

1998

"... In PAGE 12: ... Finally, gure 15 shows a multiresolution of a medical model (an image of the voxelization of a skull from a computerized tomography). Table2 lists the same parameters as the previous table, but here for the surface models.... ..."

Cited by 2