### Table 4: Elementary School Non-Linear Production Function

in Enhancing our Understanding of the Complexities of Education: "Knowledge Extraction from Data" using

### Table 2. Comparisons of Computation Costs in Linear and Non-linear Schemes

"... In PAGE 9: ...The execution e ciency of Linear, Regular, and Adaptive techniques, according to our experiments,isshown in Table2 . Wereporttwo ratios R=L and A=L.... In PAGE 9: ... We included Regular Skip with a minimum skipping distance of 2 in our study to investigate the minimum saving guaranteed by the non-linear approach. The results are shown in Table2 . We observe that Regular Skip reduces the costs of the linear technique in half, 53.... In PAGE 9: ...3% in terms of total execution time and 51.0% in terms of total number of frame comparisons (see columns 4 and 9 of Table2 ). This is consistent with our analysis in the last section.... In PAGE 9: ... News Conference One would expect Adaptive Skip to perform signi cantly better than Regular Skip with a minimum skipping distance. The results for Adaptive Skip are also included in Table2 . It shows that on average AdaptiveSkipsave more than 80% of the computation when compared with the linear approach (see columns 6 and 11 of Table 2).... In PAGE 10: ...3. Adaptiveness To examine the adaptiveness of Adaptive Skip to various types of videos, the Adaptive Linear ratio in terms of total execution time (column 6 of Table2 ) is plotted in Figure 6 for di erentaverage shot lengths in number of frames (column 12 of Table 2). We skip the extreme data from apos;TV Commercials apos; and apos;News Conference apos; to makethese graphs unbiased.... In PAGE 10: ...3. Adaptiveness To examine the adaptiveness of Adaptive Skip to various types of videos, the Adaptive Linear ratio in terms of total execution time (column 6 of Table 2) is plotted in Figure 6 for di erentaverage shot lengths in number of frames (column 12 of Table2 ). We skip the extreme data from apos;TV Commercials apos; and apos;News Conference apos; to makethese graphs unbiased.... ..."

### Table 2: Sub-band model identification rate for both linear and non-linear merging techniques Baseline 84.5

"... In PAGE 5: ...This table shows that for voting merger the 7-band model outperforms other band-based models and for this reason 7-band model is used throughout this study for the sub-band based voting merger. Table2 also illustrates that for both weighted and un-weighted merger 2-band model produced the best results. The non-linear merger on the other hand, seems to significantly outperform the linear mergers for all band-based models.... ..."

### Table 7 The non-linearity

"... In PAGE 15: ...ig. 20. Mean pulse height/energy vs. energy for pions. example, in the case of 8 : 2 con quot;guration of T411 beam test, the average density of the calorimeter module was 6.69 g/cm3 ( Table7 ). SPACAL group obtained lateral size parameter j1 quot;17.... ..."

### Table 4: Average number of Newton iterations for solving the linearized model in non-linear Fair-estimation.

in Solution of Linear Programming and Non-Linear Regression Problems Using Linear M-Estimation Methods

1999

"... In PAGE 109: ...Table4 : Results for the updating routine of the software package when used as a tool for nding L from scratch. Times are given in seconds.... ..."

### Table 1. The Calculated Results for Analyzed Data-Set

2000

"... In PAGE 9: ... In order to have easy interpretable models, we have fixed the maximal number of terms in the equation to be equal to 8 and the maximum degree of polynoms to be equal to 3. The calculations performed using the select params option of the ANALYSIS are summarized in Table1 . The number of stored models was 3.... In PAGE 9: ... It was shown that the use of significant variables, as detected by MUSEUM, = improved PLS results (compare data in column 7 vs. column 6 in Table1 ). The similar tendency was also observed if only variables found to be relevant by the PNN algorithm were used in the cross-validation calculations (compare the last and 7 columns of Table 1).... In PAGE 12: ... b Number of significant PLS components. c The cross-validated q2 calculated using input variables optimized by MUSEUM approach (unless not stated otherwise the PLS results are from Table1 and 15 of (2)). d Number of input variables selected by PNN.... ..."

Cited by 2

### Table 11: Observed number of non-linear iterations CXD8D2D0, grid point errors and respective orders of con- vergence for both steps of QSC on Problem 6.

2004

"... In PAGE 25: ...) Due to the simple shape of the solution, the application of the adaptive technique of Section 3 to Problem 6 resulted in a uniform partition, that is, the drift corresponding to the initial uniform partition was below the adaptive tech- nique tolerance D8D3D0 BP BDBCA0BE. Table11 shows that the QSC method applied to Problem 6 exhibits optimal convergence. It is worth noting that the second step of QSC requires fewer non-linear iterations than the first step, a fact which can be attributed to the choice of initial guess for the second step, as discussed above.... ..."

### Table 2: Throughput optimization of non-linear real-life designs with unrestricted amount of unfolding used: ICPL - initial critical path length, ! 0 - arbitrary speed-up

"... In PAGE 4: ... Table 1 shows throughput im- provement achieved using the best previous approach by [2] and using the new approach with optimization engine of general non- linear computation with no unfolding. Table2 shows throughput improvement achieved for non-restricted amount of unfolding. When no unfolding is used, our approach reduces the critical path lengths from the technique of [2] by 35 % on average.... ..."

### Table 2: Throughput optimization of non-linear real-life designs with unrestricted amount of unfolding used: ICPL - initial critical path length, ! 0 - arbitrary speed-up

"... In PAGE 4: ... Table 1 shows throughput im- provement achieved using the best previous approach by [2] and using the new approach with optimization engine of general non- linear computation with no unfolding. Table2 shows throughput improvement achieved for non-restricted amount of unfolding. When no unfolding is used, our approach reduces the critical path lengths from the technique of [2] by 35 % on average.... ..."

### Table 2. Ranking the feature selection methods based on their mean RMSE performance on all 20 datasets. The asterisks denote models with non-linear features, while the number in the parenthesis present the number of the linear features.

"... In PAGE 3: ... In this particular experiment (12 hours ahead), only one feature is selected. Table2 presents the performance of the FS techniques as an average over all 20 datasets, while Table A.1 has the features selected by each one of the techniques.... ..."