### Table 3. Percent Accuracy of Distance Estimates for Hits.

2001

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### Table 2. Distance estimation RMSE cm.

"... In PAGE 4: ... Four test cases #28not on the two lines#29 were recorded as well. Table2 shows the RMSE from applying three types of MBL, with varying parameters. In this table, k denotes the number of neighbors for local regression and nearest neighbor, while for kernel regression it denotes the power in equation #282#29.... ..."

### Table 3: Average eror in Route Distance estimates

"... In PAGE 5: ...067, d=.82)- se Table3 . However the non-parametric test was not significant.... ..."

### Table 4: Percent Relative eror in Distance Estimates

### Table 1. The distance estimation methods used in all comparative Studies

"... In PAGE 3: ...EVALUATION OF THE DISTANCE ESTIMATION METHODS The performance of the different methods was evaluated in three compara- tive studies. The results presented here are for the five methods summarized in Table1 . In addition, all studies included the JC distance method.... ..."

### Table 1: Features for a belief state distance estimation.

2006

"... In PAGE 16: ... 3.3 Summary of Methods for Distance Estimation Since we explore several methods for computing belief state distances on planning graphs, we pro- vide a summary of the choices we must consider, listed in Table1 . Each column is headed with a choice, containing possible options below.... In PAGE 17: ...Table 1: Features for a belief state distance estimation. In the next section we will also expand upon how to aggregate distance measures as well as discuss the remaining columns of Table1 . We will present each type of planning graph: the single planning graph (SG), multiple planning graphs (MG), and the labelled uncertainty graph (LUG).... ..."

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### Table 1: Features for a belief state distance estimation.

2006

"... In PAGE 16: ... 3.3 Summary of Methods for Distance Estimation Since we explore several methods for computing belief state distances on planning graphs, we pro- vide a summary of the choices we must consider, listed in Table1 . Each column is headed with a choice, containing possible options below.... In PAGE 17: ...Table 1: Features for a belief state distance estimation. In the next section we will also expand upon how to aggregate distance measures as well as discuss the remaining columns of Table1 . We will present each type of planning graph: the single planning graph (SG), multiple planning graphs (MG), and the labelled uncertainty graph (LUG).... ..."

Cited by 22

### Table 1: Features for a belief state distance estimation.

2006

"... In PAGE 16: ... 3.3 Summary of Methods for Distance Estimation Since we explore several methods for computing belief state distances on planning graphs, we pro- vide a summary of the choices we must consider, listed in Table1 . Each column is headed with a choice, containing possible options below.... In PAGE 17: ...Table 1: Features for a belief state distance estimation. In the next section we will also expand upon how to aggregate distance measures as well as discuss the remaining columns of Table1 . We will present each type of planning graph: the single planning graph (SG), multiple planning graphs (MG), and the labelled uncertainty graph (LUG).... ..."

Cited by 22

### Table 3: Jukes-Cantor pairwise distance estimates.

2006

"... In PAGE 33: ... An alternative approach is to estimate pairwise distances between species i, j using the formula in Proposition 12. The resulting metric on the set X = {gg, hs, mm, pt, rn, cf, dr, tn, tr, xt} is given in Table3 . For example, the pairwise alignment between human and chicken (extracted from the multiple alignment) has n = 14202 positions, of which k = 7132 are different.... ..."

Cited by 5