### TABLE 3. In the following sections, we will visualize the distance functions by their level sets. In 2D, we plot the contour lines, and in 3D, we plot the isosurfaces.

### Table 2: Key algorithms of a N-dimensional DT-Grid. The associated time com- plexities are derived in subsequent sections.

2004

"... In PAGE 10: ... In particular, we describe a generic algorithm for rebuild- ing the tubular grid, which can be used independently of the method employed for re-initializing the level set function to a signed distance function. Table2 gives an overview of the operations and their associated time complexities. 3.... ..."

### Table 2: Key algorithms of a N-dimensional DT-Grid. The associated time complexities are derived in subsequent sections.

2004

"... In PAGE 11: ... In particular, we describe a generic algorithm for rebuilding the tubular grid, which can be used independently of the method employed for re-initializing the level set function to a signed distance function. Table2 gives an overview of the operations and their associated time complexities.... ..."

### Table 1. Classification results with the test database Wavelet and Level of Distance functions

"... In PAGE 4: ... Table1 indicates the classification results over the test sets using the minimum distance classifier as described in the previous section. The overall classification results were obtained by averaging the results over the 20 different partitions of the data set into sample and test sets.... In PAGE 4: ... A performance index of 100% indicates a perfect classification. The first observation to Table1 is that the DMWT methods always outperform the DWT, which is consistent with our expectation. It can be seen that multiresolution decomposition with two or three levels is superior to with one level only .... In PAGE 4: ... A comparison of the results between several different distance metrics indicates that Euclidean distance has the worst performance and the other three distance measures perform well with similar performance. Table1 also indicates that the different multiwavelets give a similar performance. ... ..."

### Table 3: Itakura-Saito distance of speech

"... In PAGE 4: ... These functions are shown in Figure 5. The Itakura-Saito objective quality measurements for these different data sets are included in Table3 . Because the distortion levels are extremely varied across the wave- form, both the average mean and maximum distances within the distorted corpus are calculated.... ..."

### Table 1: Number of clusters in some benchmarks functions.

"... In PAGE 4: ... We have implemented a classical algorithm for computing the transitive closure [6] and tried it on benchmark functions to see how many disjoint clusters we obtain for different values of k. Table1 shows the results. Columns 2 and 3 give the number of cubes in the covers for on- and off-set of the benchmark functions, correspondently, computed by the two- level AND-OR minimizer Espresso [7].... In PAGE 4: ... If clustering is added as a preprocessing step (with distance k = 0), then the ob- jects are equivalence classes of the on-set of the function, determined by the algorithm for computing the transitive closure. As shown in Table1 , clustering greatly reduces the number of objects to consider. In the next section we demonstrate that this leads to a considerable reduction in the run-time of AOXMIN-MV algorithm.... ..."

### Table 2. u-plot Kolmogorov Distance Coverage Function Data Set 2 Data Set 3 Data Set 4

1996

Cited by 6

### Table 2. u-plot Kolmogorov Distance Coverage Function Data Set 2 Data Set 3 Data Set 4

1996

Cited by 6

### Table 2. u-plot Kolmogorov Distance Coverage Function Data Set 2 Data Set 3 Data Set 4

1996

Cited by 6

### Table 2: Classification results of five distance functions

2005

"... In PAGE 4: ... To get average values over a number of data sets, we use each raw data set as a seed and generate 50 distinct data sets that include noise and time shifting. The results are shown in Table2 . For two data sets, EDR performs the best, showing that it is superior to the other distance functions... ..."

Cited by 21