### Table 3: Parallel Shortest Path Algorithm

1997

"... In PAGE 4: ... Our parallel shortest path algorithm uses the SPMD model for which each processor solves the portion of the shortest path tree on its subnetwork for each source. The outline of the algorithm is given in Table3 . Each processor repeatedly solves for shortest paths for its assigned subnetwork.... ..."

Cited by 7

### Table 3: Parallel Shortest Path Algorithm

1997

"... In PAGE 4: ... Our parallel shortest path algorithm uses the SPMD model for which each processor solves the portion of the shortest path tree on its subnetwork for each source. The outline of the algorithm is given in Table3 . Each processor repeatedly solves for shortest paths for its assigned subnetwork.... ..."

Cited by 7

### Table 4: Steiner vs Shortest Paths Trees

2003

"... In PAGE 113: ... The parameters were the same as used in the romrOn case described above. The results, given in Table4 , showed that the successful delivery of packets to the receivers was very similar in both of the two cases, but other statistics gathered indicate the superiority of the use of the Steiner trees. The average amount of time a tree set remained active before a new tree set was distributed was 8% shorter in the shortest paths trees than in the Steiner tree version.... ..."

Cited by 3

### Table 5. Average shortest path length of accepted requests in the NSFNET network.

Cited by 1

### Table 3 Performance of Shortest Path Search

"... In PAGE 2: ... This allows us to test if a general link classification model could be obtained to apply to unseen Websites. We build different versions of weighted directed graphs using each of the three types of classifiers and compare the performances of the algorithms on the different graphs Table3 and 4. As can be seen from the results, the SPS and DMST algorithms using weighted graph models outperform the simple BFS significantly.... ..."

Cited by 1

### TABLE 1 Brier score: maximum entropy distributions

2004

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### Table 1 The duality of maximum entropy and maximum likelihood is an example of the more general

1996

"... In PAGE 10: ... This result provides an added justi cation for the maximum entropy principle: if the notion of selecting a model p ? on the basis of maximum entropy isn apos;t compelling enough, it so happens that this same p ? is also the model which, from among all models of the same parametric form (10), can best account for the training sample. Table1 summarizes the primal-dual framework wehave established. 3.... ..."

Cited by 614

### Table 3. Generated paths and each path costs k-shortest path method The proposed method Paths

"... In PAGE 9: ... from the k-shortest algorithm are concentrated, but the paths from the proposed method are scattered over the whole network. Table3 summarizes the values of path cost and links on the path. The first two paths have the same values of path costs for all method, but the rest three path costs of the proposed algorithm are higher than those of k-shortest path one because it generates the paths in order of degree of path overlap, not in order of path costs.... In PAGE 10: ... prohibitions at node 11, node 14 and node 23 exist, with the value of 50% of path overlap. Table 4 demonstrates that five dissimilar paths are also generated, and that last 2 paths are different from those of paths in Table3 due to turning restrictions. We note here that the 5th path has U-turn movement at node 24 because of left-turn prohibition at node 23.... ..."

### Table 4: The average log likelihoods of the uniform distribution, the multinomial distribution and the maxi- mum entropy model.

2006

"... In PAGE 4: ... We used the average log likelihood for the evaluation mea- surements. Table4 shows the results. The average log likelihoods of the maximum entropy models for the test samples were higher than uniform and multinomial distri- butions.... ..."

Cited by 1