### Table 2 Average performance of probabilistic scheduling Rule

2001

"... In PAGE 5: ... In order to report aggregate measures, the best result obtained for a problem is set in relation to the outcome of the deterministic version of the FCFS rule. These values, averaged over the instances of the benchmark set, are shown in Table2 with respect to the four objectives under consideration. The results shown for FCFS verify that a probabilistic schedule builder can produce con- siderable improvements against a deterministic one.... ..."

### Table 2: Comparison of performance on practical examples;; the probabilistic

1998

"... In PAGE 8: ... We also tried a modi ed version of the EA which rst runs APGAN and then inserts the computed topological sort into the initial population. Table2 shows the results of applying GDPPO to the schedules generated by the various heuristics on several practical SDF graphs;; the satellite re- ceiver example is taken from [16], whereas the other examples are the same as considered in [3]. The probabilistic algorithms ran once on each graph and were aborted after 3000 tness evaluations.... In PAGE 8: ... Additionally, an exhaustive searchwithamaximum run-time of 1 hour was carried out;; as it only com- pleted in two cases 3 , the search spaces of these problems seem to be rather complex. In all of the practical benchmark examples in Table2 the results achieved by the EA equal or surpass those generated by RPMC. Compared to APGAN on these practical examples, the EA is neither inferior nor superior;; it shows both better and worse performance in two cases each.... ..."

Cited by 12

### Table 9: Performance of probabilistic, predictive, and omniscient resynchronization policies at A = for the extreme scenario, when is changed to 10?2.

"... In PAGE 21: ...1, predictive policies always perform at least as well as their aggressive and conservative counterparts). Table9 , which compares the effectiveness of the predictive policies with probabilistic and omniscient policies3 for the case when = 10?2, corroborates our expectation.... ..."

### Table 5: Performance of Probabilistic Categorisation on the Partial DMOZ Collection. The structure of the table is described further in the caption of Table 1.

2002

Cited by 5

### Table 2. Comparison of performance on practical examples;; the probabilistic algo-

1998

"... In PAGE 8: ... We also tried a slightly modi ed version of the Evolutionary Algorithm which rst runs APGAN and then inserts the computed topological sort into the initial population. Table2 shows the results of applying GDPPO to the schedules generated by the various heuristics on several practical SDF graphs;; the satellite receiver ex- ample is taken from [15], whereas the other examples are the same as considered in [2]. The probabilistic algorithms ran once on each graph and were aborted after 3000 tness evaluations.... In PAGE 8: ... Additionally, an exhaustive searchwitha maxi- mum run-time of 1 hour was carried out;; as it only completed in two cases 5 , the search spaces of these problems seem to be rather complex. In all of the practical benchmark examples that makeup Table2 the results achieved by the Evolutionary Algorithm equal or surpass the ones generated byRPMC.Compared to APGAN on these practical examples, the Evolution- ary Algorithm is neither inferior nor superior;; it shows both better and worse performance in two cases each.... ..."

Cited by 2

### Table II Performance of neural network, probabilistic, and hybrid systems

### Table 1. List of detectors combined probabilistically, and resulting performance of composite system.

### TABLE V Performance of learned probabilistic strategies against the dealer apos;s strategy.

1998

Cited by 4

### Table 1n3a Comparison of the performance of Neural and Probabilistic RF for 10n25 training

1995

Cited by 1

### Table 4.8. Performance for Different Probabilistic Models Corpus F (Rknown=Runknown)

2006