### Table 6 Decision rules for the efficient retrieval of direction relations

### Table 6 Decision rules for the efficient retrieval of direction relations

### Table 6 Decision rules for the efficient retrieval of direction relations

### Table 1: Side-by-side comparison of the coding efficiency by the original mode decision and our proposed mode deci-

1997

"... In PAGE 6: ... However, the collective saving over a sequence of 300 frames could be significant. Table1 shows the simulation results on some standard low bit-rate test conditions1. In the table, the significant difference in SNR (greater than 0.... ..."

Cited by 2

### Table 2. The efficiency of three naive Bayes decision rules, for five different naive Bayes classifiers trained on subsets of the UCI mushrooms data set.

2005

"... In PAGE 65: ...3 indicate that case based precision estimates may allow for more robust decision rules when the loss function is asymmetrical. Table2 shows that overarather large domain in the UCI mushrooms data set (five classifiers trained on different portionsofthedatabaseandeachappliedto100cases)theBayesianbootstrapbaseddecision rule has better specificity, and lower average loss for variable misclassification costs. A plausible explanation for this is the tendency of the naive Bayes classifier to probability overshoot , i.... ..."

### Table A11. School-level decision making autonomy and technical efficiency of the median school Coefficient estimates from correlations between technical efficiency estimates and institutional indicators

### Table 2. Comparison of solving efficiency of the original model and the model with learned constraints solved by RSAT solver (the smallest #backtracks / #decisions / runtime is in bold).

"... In PAGE 11: ... We used one of the winning solvers in the SAT-RACE 2006 competition, RSat [12], to validate our hypothesis, that the learned constraints may also improve efficiency of SAT solvers. Table2 shows the comparison of the number of backtracks, the number of decision (choice) points, and runtime for the original SAT problem and for the SAT problem with the added implied constraints. Again, we used the problem classes from [9].... ..."

### Table 10: Decisions at second stage. Node j corresponds to demand state, l is the work capability, v is the initial capability and s is the operation efficiency.

### Table 10: Decisions at second stage. Node j corresponds to demand state, l is the work capability, v is the initial capability and s is the operation efficiency.

2007

### Table 5: Contributions to the constrained efficient frontier

2000

"... In PAGE 29: ... In order to decide which portfolio of assets to buy however, a decision-maker examining the efficient frontier needs a good distribution of points over this frontier in order to make an informed decision. This distribution of points over the frontier is illustrated numerically in Table5 . In that table we give, for each data set, for each of the three heuristics, the number of efficient points that they individually contribute to the pooled set of efficient points.... ..."

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