### Table1. Scale of the original and the current Great Buddha

"... In PAGE 5: ...most experts as a correct parameter. Table1 shows some value used in our morphing together with the value from the current Great Buddha. Table 1 included that the size of the current Great Buddha is slightly different from that of the original one.... In PAGE 5: ... Table 1 shows some value used in our morphing together with the value from the current Great Buddha. Table1 included that the size of the current Great Buddha is slightly different from that of the original one. The morphing process contains two steps.... ..."

### Table1. Scale of the original and the current Great Buddha

"... In PAGE 5: ...most experts as a correct parameter. Table1 shows some value used in our morphing together with the value from the current Great Buddha. Table 1 included that the size of the current Great Buddha is slightly different from that of the original one.... In PAGE 5: ... Table 1 shows some value used in our morphing together with the value from the current Great Buddha. Table1 included that the size of the current Great Buddha is slightly different from that of the original one. The morphing process contains two steps.... ..."

### Table 5 shows the Precision and Recall performance of the dynamic combined and R1 method (which performed best) over the corpus. As we can see we have an increase in Recall for every program except 7. The Precision figures are lower but they have not affected the overall Precision value greatly.

2000

"... In PAGE 7: ... Table5 : Combined and R1 Methods, Dynamic Threshold One of the aspects of this work that was of interest was to identify what programs or program genres give the best and the worst performance. Clearly, the combined strategy has a negligible effect on precision for all programs except program 1, which is RTE 1pm News, while it has a positive effect on recall, the greatest impact being on that same RTE 1pm News program.... ..."

Cited by 25

### (Table 6). Interestingly students from this project and the ISDN videoconferencing placed a great deal of value in being able to share and have dialogue with students from other locations. From a student point of view, using technology to enable this sharing and dialogue appears to be a more powerful learning feature than the learning of content.

### Table 3. Labeling of the great bracket using fuzzy logic

in Automatic Extraction of Printed Mathematical Formulas Using Fuzzy Logic and Propagation of Context

"... In PAGE 13: ... We finally take the maximal value (disjunction of symbol types). Thus, the membership degree of that c to a class of symbol is defined as follows : mMS(c) = Max(Min(mMS,R(c),mMS,A(c),mMS,D(c)) = Max(mSP(c), mIS(c), mRS(c), mHFB(c), mGD(c), mSD(c), mOP(c)) In Table3 , we present the results obtained after a fuzzy labeling of the great parenthesis,... ..."

### Table 1 shows the fitting result. From the result, we can see that the MI has some significant relationship with IP boundary, and also the preprocessing contributes great in the fitting. And also the consideration of surround MI value is also useful.

"... In PAGE 3: ... Note that the smoothing algorithm is also employed to get more rational IP prediction. Table1 : polynomial fitting result from MI to IP boundary MI value Considered MI value Multiple R MS Residual Left: 0, Mid: 1, Right: 0 0.195258 0.... ..."

### Table 5: Fraction of index blocks, selector groups, and underlying pointers compressed using different values of m, summed across all four test collections, for method 1- 2-4 m, escape . The great majority of pointers are stored in blocks for which m is between 3 and 5.

"... In PAGE 6: ... tiveness, and the final row of Table 3 reflects compression rates within about 0:5 bits per pointer of those attained by a Golomb code, and equal to the Golomb code on the het- erogenous collection TREC12. Finally in this section, Table5 shows the distribution of values of m, summed across the four collections. Col- umn two of that table shows that the great majority of in- dex blocks are coded using m = 1.... ..."

### Table 1: Shown is classi cation error for di erent values of lambda on the full 20 newsgroups classi cation task. The rst column gives the regularization parameter used. The second column gives training error. The third column gives test error. The fourth column gives the LOOCV error bound. The LOOCV error bound greatly over-estimates generalization error for small values of lambda.

2003

"... In PAGE 3: ... We could have almost always done better (!) by simply chosing the largest lambda from the set of lambdas that gave the lowest error (often, many di erent lambdas yielded zero training error). Table1 gives the results for the full 20 class classi cation task. The lowest test error (15.... ..."

### Table 2.2. Convergence thresholds The values of the mean eigenvalue, , are also of great practical value in detecting clusters of nearly identical eigenvalues. Since early cluster detection can greatly reduce the amount of work done, we use a simple heuristic scheme that chooses whichever of A1 or A2 has all of its eigenvalues on the same side of 0 as the mean eigenvalue of A. Furthermore, j j is always a lower bound on the spectral radius of the original matrix. A running estimate, , of the largest mean eigenvalue in magnitude from already completed divides is kept. When the bounds used in the Scaling step of ISDA indicate that all the eigenvalues of the current subproblem are either O(u ) or within O(u ) of each other (recall u is the machine epsilon), then the subproblem is declared to have clustered eigenvalues and to be \done. quot; Thus, for instance, matrices with exponentially distributed eigenvalues did not prove to be as ex- pensive computationally as might be expected. A matrix with exponentially distributed eigenvalues

1991

Cited by 27

### Table 3: Increase in the Private Debt Burden Due to Deflation: The Depression of 1921-22 vs. the Great Depression7

"... In PAGE 6: ... We measure the increase in the debt burden as the increase in the real value of debt (relative to output) due to deflation over the first two years of each depression. Table3 shows the initial stock of debt relative to output at the price level peak prior to each depression, as well as the percentage change in prices in the first two years of each depression, the implied percentage increase in the debt burden relative to initial output, and the percentage change in real output. The most striking feature of these data is that the 6Some economists have also suggested that high real interest rates were an important contributing factor to the Great Depression.... ..."