### Table 5: Additional facets related to algorithms for PDE boundary value problems. Facet Terms

### Table 2: Test result from a real-world situation of ten transceivers in the city of Vienna. The header lines contain the names of the transceivers (B-W). For each transceiver, the left column shows the listed neighborhood relations (Rl), and the right column shows the listed neighborhood relations found by our algorithm also (Tl), and relations found additionally (Tr or Tf).

2001

"... In PAGE 19: ... With no possibility to distinguish Tr and Tf automatically, we expect at least that the found neighborhood relations contain the listed neighborhood relations completely: Tl =! Rl. In Table2 we see the result of the calculation of Tc for each transceiver. We found all relations of Rl in Tc for all ten receivers, i.... ..."

Cited by 1

### Table 1.1 LAPACK codes for computing eigenpairs of a symmetric tridiagonal matrix of dimension n, see also [2, 4]. Note that in addition to computing all eigenpairs, inverse iteration and the MRRR algorithm (MRRR = Multiple Relatively Robust Representations) also allow the computation of eigenpair subsets at reduced cost.

### Table 1: Parameter settings for the three algorithms; in addition l opt = 50, l fails = 20, cr trials = 10 for M-PAES (see [6] for the meaning of these parameters). The three gures for number of evaluations relate to the three di erent problem sizes, 10, 25, and 50 vertices, respectively

2001

"... In PAGE 3: ... There is no correlation between the edge weight components in the concave graphs, and the other parameters used for generating these graphs were = 0:1; 0:05; 0:03 and = 0:25; 0:2; 0:125 for the 10vConc, 25vConc and 50vConc graphs respectively. Algorithm parameters and experiments The parameters used for each of PAES, AESSEA, and M-PAES are given in Table1 . Each of the algorithms was given 30 independent runs on each of the 15 in- stances and the nondominated archive returned by each algorithm from each run was stored for statis- tical analysis, and comparison with the other non-EA approaches.... ..."

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### Table 5 Additive algorithms.

1997

Cited by 52

### Table 2 tabulates the experimental results obtained from both corpora using Bayesian Networks, Support Vector Machines and our hybrid algorithm of using Bayesian inference knowledge and inserting it into the SVM hyperparameters, noted as B-SVM. For the task of identifying a valid SF using additional unlabeled examples, we have conducted the experiments using approximately 6.500 unlabeled instances from each corpus. Additionally, in order to obtain a more inclusive view of the task, we provide results using statistical machine learning algorithms such as relative frequency

2002

"... In PAGE 7: ...-2+3] 85.7 89 93.4 80.3 72.6 78.7 Table2 : Experimental results obtained from the two corpora By observing the obtained results, we could claim that both BBN and SVM perform significantly better than the other machine learning algorithms by a factor that varies from 5 to almost 30%, a fact that supports the argue that BBN and SVM are well suited for the task of verb subcategorization identification (Maragoudakis et al, 2001). Furthermore, by incorporating bayesian knowledge into the SVM classifier and using a set of unlabeled examples, we achieve a 3-6% improvement.... ..."

Cited by 1

### Table 5 Additional titles for updating.

1995

"... In PAGE 10: ....3. Folding-In. Suppose the ctitious titles listed in Table5 are to be added to the original set of titles in Table 2. While some titles in Table 5 use terms related to nonlinear systems or di erential equations, notice the di erent meaning of the speci c term ordinary in book titles B19 and B20 as opposed to book titles B8 and B10.... In PAGE 10: ....3. Folding-In. Suppose the ctitious titles listed in Table 5 are to be added to the original set of titles in Table 2. While some titles in Table5 use terms related to nonlinear systems or di erential equations, notice the di erent meaning of the speci c term ordinary in book titles B19 and B20 as opposed to book titles B8 and B10.AswithTable 2, all underlined words in Table 5 are considered signi cant since they appear in more than one title (across all 20 titles from Tables 2 and 5).... In PAGE 10: ... While some titles in Table 5 use terms related to nonlinear systems or di erential equations, notice the di erent meaning of the speci c term ordinary in book titles B19 and B20 as opposed to book titles B8 and B10.AswithTable 2, all underlined words in Table5 are considered signi cant since they appear in more than one title (across all 20 titles from Tables 2 and 5). Folding- in (see Section 2.... In PAGE 11: ... As discussed in [23], the the accuracy of SVD-updating approaches can be easily compared to that obtained when the SVD of ~ A is explicitly computed. Suppose the titles from Table5 are combined with those of Table 2 in order to create a new 16 20 term-document matrix ~ A.Following Figure 1, we then construct the best rank-2 approximation to ~ A, ~ A 2 = ~ U 2 ~ 2 ~ V T 2 : (9) Figure 8 is a two-dimensional plot of the 16 terms and 20 documents (book titles) using the elements of ~ U 2 and ~ V 2 for term and document coordinates, respectively.... In PAGE 11: ... Notice the di erence in term and document positions between Figures 7 and 8. Clearly, the the new book titles from Table5 have helped rede ne the underlying laten t structure when the SVD of ~ A is computed. That is, one can discuss ordinary algorithms and ordinary di erential equations in di erentcontexts.... In PAGE 17: ....4. SVD-Updating Example. To illustrate SVD-updating, suppose the ctitious titles in Table5 are to be added to the original set of titles in Table 2. In this example, only documents are added and weights are not adjusted, hence only the SVD of the matrix B in Equation (10) is computed.... ..."

Cited by 409

### Table 5 Additional titles for updating.

1995

"... In PAGE 10: ....3. Folding-In. Suppose the ctitious titles listed in Table5 are to be added to the original set of titles in Table 2. While some titles in Table 5 use terms related to nonlinear systems or di erential equations, notice the di erent meaning of the speci c term ordinary in book titles B19 and B20 as opposed to book titles B8 and B10.... In PAGE 10: ....3. Folding-In. Suppose the ctitious titles listed in Table 5 are to be added to the original set of titles in Table 2. While some titles in Table5 use terms related to nonlinear systems or di erential equations, notice the di erent meaning of the speci c term ordinary in book titles B19 and B20 as opposed to book titles B8 and B10. As with Table 2, all underlined words in Table 5 are considered signi cant since they appear in more than one title (across all 20 titles from Tables 2 and 5).... In PAGE 10: ... While some titles in Table 5 use terms related to nonlinear systems or di erential equations, notice the di erent meaning of the speci c term ordinary in book titles B19 and B20 as opposed to book titles B8 and B10. As with Table 2, all underlined words in Table5 are considered signi cant since they appear in more than one title (across all 20 titles from Tables 2 and 5). Folding- in (see Section 2.... In PAGE 11: ... As discussed in [23], the the accuracy of SVD-updating approaches can be easily compared to that obtained when the SVD of ~ A is explicitly computed. Suppose the titles from Table5 are combined with those of Table 2 in order to create a new 16 20 term-document matrix ~ A. Following Figure 1, we then construct the best rank-2 approximation to ~ A, ~ A2 = ~ U2 ~ 2 ~ V T 2 : (9) Figure 8 is a two-dimensional plot of the 16 terms and 20 documents (book titles) using the elements of ~ U2 and ~ V2 for term and document coordinates, respectively.... In PAGE 11: ... Notice the di erence in term and document positions between Figures 7 and 8. Clearly, the the new book titles from Table5 have helped rede ne the underlying latent structure when the SVD of ~ A is computed. That is, one can discuss ordinary algorithms and ordinary di erential equations in di erent contexts.... In PAGE 17: ....4. SVD-Updating Example. To illustrate SVD-updating, suppose the ctitious titles in Table5 are to be added to the original set of titles in Table 2. In this example, only documents are added and weights are not adjusted, hence only the SVD of the matrix B in Equation (10) is computed.... ..."

Cited by 409

### Table 5 Additional titles for updating.

1995

"... In PAGE 10: ....3. Folding-In. Suppose the ctitious titles listed in Table5 are to be added to the original set of titles in Table 2. While some titles in Table 5 use terms related to nonlinear systems or di erential equations, notice the di erent meaning of the speci c term ordinary in book titles B19 and B20 as opposed to book titles B8 and B10.... In PAGE 10: ....3. Folding-In. Suppose the ctitious titles listed in Table 5 are to be added to the original set of titles in Table 2. While some titles in Table5 use terms related to nonlinear systems or di erential equations, notice the di erent meaning of the speci c term ordinary in book titles B19 and B20 as opposed to book titles B8 and B10. As with Table 2, all underlined words in Table 5 are considered signi cant since they appear in more than one title (across all 20 titles from Tables 2 and 5).... In PAGE 10: ... While some titles in Table 5 use terms related to nonlinear systems or di erential equations, notice the di erent meaning of the speci c term ordinary in book titles B19 and B20 as opposed to book titles B8 and B10. As with Table 2, all underlined words in Table5 are considered signi cant since they appear in more than one title (across all 20 titles from Tables 2 and 5). Folding- in (see Section 2.... In PAGE 11: ... As discussed in [23], the the accuracy of SVD-updating approaches can be easily compared to that obtained when the SVD of ~ A is explicitly computed. Suppose the titles from Table5 are combined with those of Table 2 in order to create a new 16 20 term-document matrix ~ A. Following Figure 1, we then construct the best rank-2 approximation to ~ A, ~ A2 = ~ U2 ~ 2 ~ V T 2 : (9) Figure 8 is a two-dimensional plot of the 16 terms and 20 documents (book titles) using the elements of ~ U2 and ~ V2 for term and document coordinates, respectively.... In PAGE 11: ... Notice the di erence in term and document positions between Figures 7 and 8. Clearly, the the new book titles from Table5 have helped rede ne the underlying latent structure when the SVD of ~ A is computed. That is, one can discuss ordinary algorithms and ordinary di erential equations in di erent contexts.... In PAGE 17: ....4. SVD-Updating Example. To illustrate SVD-updating, suppose the ctitious titles in Table5 are to be added to the original set of titles in Table 2. In this example, only documents are added and weights are not adjusted, hence only the SVD of the matrix B in Equation (10) is computed.... ..."

Cited by 409

### Table 5 Additional titles for updating.

1995

"... In PAGE 10: ....3. Folding-In. Suppose the ctitious titles listed in Table5 are to be added to the original set of titles in Table 2. While some titles in Table 5 use terms related to nonlinear systems or di erential equations, notice the di erent meaning of the speci c term ordinary in book titles B19 and B20 as opposed to book titles B8 and B10.... In PAGE 10: ....3. Folding-In. Suppose the ctitious titles listed in Table 5 are to be added to the original set of titles in Table 2. While some titles in Table5 use terms related to nonlinear systems or di erential equations, notice the di erent meaning of the speci c term ordinary in book titles B19 and B20 as opposed to book titles B8 and B10.AswithTable 2, all underlined words in Table 5 are considered signi cant since they appear in more than one title (across all 20 titles from Tables 2 and 5).... In PAGE 10: ... While some titles in Table 5 use terms related to nonlinear systems or di erential equations, notice the di erent meaning of the speci c term ordinary in book titles B19 and B20 as opposed to book titles B8 and B10.AswithTable 2, all underlined words in Table5 are considered signi cant since they appear in more than one title (across all 20 titles from Tables 2 and 5). Folding- in (see Section 2.... In PAGE 11: ... As discussed in [23], the the accuracy of SVD-updating approaches can be easily compared to that obtained when the SVD of ~ A is explicitly computed. Suppose the titles from Table5 are combined with those of Table 2 in order to create a new 16 20 term-document matrix ~ A.Following Figure 1, we then construct the best rank-2 approximation to ~ A, ~ A 2 = ~ U 2 ~ 2 ~ V T 2 : (9) Figure 8 is a two-dimensional plot of the 16 terms and 20 documents (book titles) using the elements of ~ U 2 and ~ V 2 for term and document coordinates, respectively.... In PAGE 11: ... Notice the di erence in term and document positions between Figures 7 and 8. Clearly, the the new book titles from Table5 have helped rede ne the underlying latent structure when the SVD of ~ A is computed. That is, one can discuss ordinary algorithms and ordinary di erential equations in di erentcontexts.... In PAGE 17: ....4. SVD-Updating Example. To illustrate SVD-updating, suppose the ctitious titles in Table5 are to be added to the original set of titles in Table 2. In this example, only documents are added and weights are not adjusted, hence only the SVD of the matrix B in Equation (10) is computed.... ..."

Cited by 409