### Table 1: Variation in Algorithm Parameters

"... In PAGE 12: ... 6(f) to have suppressed the false alarms due to noise while retaining detection of the genuine target. The variation of algorithm parameters and the corresponding changes in detection performance are summarised in Table1 . In this case, detection performance is measured by the time to first detection in addition to the rates of missed detection and false alarm.... ..."

### TABLE 3. Variations of the MC algorithm

1994

"... In PAGE 13: ... Variations of the SMV algorithm 53 TABLE 2. Variations of the MMV algorithm 53 TABLE3 . Variations of the MC algorithm 54 TABLE 4.... ..."

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### TABLE 2. Variations of the MMV algorithm

1994

"... In PAGE 13: ...List of Tables TABLE 1. Variations of the SMV algorithm 53 TABLE2 . Variations of the MMV algorithm 53 TABLE 3.... ..."

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### TABLE 1. Variations of the SMV algorithm

1994

"... In PAGE 13: ...List of Tables TABLE1 . Variations of the SMV algorithm 53 TABLE 2.... ..."

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### Table 3. RPDS algorithm variations

### Table 4 Comparison of Variations of SDVR Algorithm

1999

"... In PAGE 6: ... Table 3 SDVR Results with Conditional Updating Table4 summarizes how training results change with each implementation as the problem complexity varies. A rea- sonable index for the problem complexity is the size of the Jacobian matrix, which is the product of the number of training patterns and the number of network parameters.... In PAGE 6: ...01. In Table4 , INC rep- resents incremental updating, CVG refers to convergent updating and CDT is for conditional updating. Table 4 Comparison of Variations of SDVR Algorithm ... ..."

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### TABLE 6. Variations of the Cost Estimation Algorithms

1994

"... In PAGE 13: ... Parameters of the Experimental Problems 57 TABLE 5. Average Number of Constraints and Average Optimal Cost for each Problem Set 63 TABLE6 . Variations of the Cost Estimation Algorithms 90 TABLE 7.... ..."

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### Table 2: Orientation Algorithm Variations Results

2002

"... In PAGE 37: ... Once again the BMP-2, T-72 and BTR-70 vehicle types were used, each vehicle being imaged from varying azimuthal directions. The algorithm results were compared with the known image angle, and the performance statistics calculated, as presented in Table2 . These evaluations were conducted external to the ATR Workbench in order to determine which would be imple- mented.... ..."

### Table 1: Computational results for di erent variations of Algorithm 1 and Algorithm 2.

"... In PAGE 16: ... The GNU gcc com- piler (under the Linux operating system) was used, with the optimization ag -O2 enabled. Table1 presents a summary of the results of experiments with the pro- posed algorithms. The rst two columns give the size of instances used.... In PAGE 16: ... All instances assume that node 1 is the source node. In columns 3 to 7 of Table1 we show the comparison of results returned by di erent variations of Algorithm 1. The second and third columns give the solutions returned by the policies depth rst search and shortest path, respectively.... In PAGE 18: ... It seams that, given the weakness of the random path policy, the ordering method becomes a signi cant parameter in the determination of the solution quality. Columns 8 to 10 of Table1 present results for the execution of Algo- rithm 2. They correspond, respectively, to the largest infeasibility, closest from source, and uniform random policies.... ..."

### Table 1: Parameter settings for the variational algorithm (the rst three apply also to GD).

"... In PAGE 11: ... The GD method was chosen as the T = 0 limit of the variational algorithm, with identical parameter settings for , ^ 0 and k (cf. Table1 ); thus, the regularized LJ potentials were used also here. The SA simulations were made using Metropolis updating, with an initial temperature T0 given by three times the modulus of the lowest energy achieved by the variational method in case A (for simplicity, the same T0 was used also for the B problems of the same size).... ..."