### Table 2. String matching and regular expression matching times. The string matching times are total times of matching 10000 patterns. text string matching regular expression matching matches time (s) time (ms)

1995

"... In PAGE 12: ... To test matching performance we implemented string matching and regular expression matching algorithms for all three data structures. The results of our tests are given in Table2 . The execution times include going through the set of matches.... ..."

Cited by 29

### Table 1: Processing throughput in MVoxels/s for the GPU and CPU implementation of the diffusion-based region growing algorithm.

2005

Cited by 2

### Table 1: Processing throughput in MVoxels/s for the GPU and CPU implementation of the diffusion-based region growing algorithm.

2005

Cited by 2

### Table 2: Evaluation of matching algorithm for pattern recognition

"... In PAGE 13: ... Notice that the high threshold for the collective probability of match implies high accuracy of rules triggered for each particular time point in the pattern. Let us now describe the results of the experiments presented in Table2 . The columns of the table represent: the pattern number (PatN), the length of the pat- tern (PatL), the number of matchings of the pattern with the theory (NumM), the average probability of guesses which leaded to matching (AvgGP), the average col- lective probability of the match (AvgMP), and the list of time points (TPts) where the pattern appeared.... In PAGE 13: ... The columns of the table represent: the pattern number (PatN), the length of the pat- tern (PatL), the number of matchings of the pattern with the theory (NumM), the average probability of guesses which leaded to matching (AvgGP), the average col- lective probability of the match (AvgMP), and the list of time points (TPts) where the pattern appeared. The pattern number stated in Table2 represents the ordering number of patterns from Figure 4. The ordering of patterns is from left to right and then from the rst to the fourth line; the pattern number 1 is in the upper left corner and the pattern number 24 is in the lower right corner.... ..."

### Table 2: Evaluation of matching algorithm for pattern recognition

"... In PAGE 15: ... Notice that the high threshold for the collective probability of match implies high accuracy of rules triggered for each particular time point in the pattern. Let us now describe the results of the experiments presented in Table2 . The columns of the table represent: the pattern number (PatN), the length of the pat- tern (PatL), the number of matchings of the pattern with the theory (NumM), the average probability of guesses which leaded to matching (AvgGP), the average col- lective probability of the match (AvgMP), and the list of time points (TPts) where the pattern appeared.... In PAGE 15: ... The columns of the table represent: the pattern number (PatN), the length of the pat- tern (PatL), the number of matchings of the pattern with the theory (NumM), the average probability of guesses which leaded to matching (AvgGP), the average col- lective probability of the match (AvgMP), and the list of time points (TPts) where the pattern appeared. The pattern number stated in Table2 represents the ordering number of patterns from Figure 4. The ordering of patterns is from left to right and then from the rst to the fourth line; the pattern number 1 is in the upper left corner and the pattern number 24 is in the lower right corner.... ..."

### Table 2 Parallel deterministic algorithms for maximal matchings Authors Time Processors Year Families of graphs

"... In PAGE 113: ...mong processors and greedy procedures to achieve goals (2.3) or (3.1) [GOTO81], [CHRI90]. We refer to these algorithms as SUBD-EXCH and GREEDY, respectively. Table2 summarizes the allocation algorithms we have implemented in the Domain Decomposer. Stochastic optimization techniques for the allocation problem have been considered explicitly or implicitly by several authors.... In PAGE 114: ...Table2 : Domain decomposition allocation strategies Name Description RANDOM naive SHIFT Di ! i ? 1 processor EXPLICIT H Munkres algorithm for (4.1) SUBD EXCH implicit algorithm for (4.... ..."

### Table 1: The performance of illumination corrected pattern matching with normal pattern matching and

1998

"... In PAGE 7: ...For the hundred locations, where the distances between the detected locations are biggest, were then visually inspected and whenever one of the algorithms performed better, its \did it better quot; score is increased by one. Table1 shows the results of these experiments. Eachentry in this table is a comparison of the distorted gray-level algorithm with the algorithm at the column top and the feature at the left of the row.... ..."

### Table 2. Pattern Matching Costs.

"... In PAGE 13: ...Table2... In PAGE 13: ... Node a68 a1 passes on the tokens with the correct number of arguments and a68a128a133 creates an instantiation of the query and adds it to the conflict set. Performing the same analysis as before, the results are presented in Table2 . It is worth noting that the number of computational steps executed by the Rete algorithm for pattern matching each query are... ..."