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Table 6. ASSOC with context-recommendation sys- tem.
"... In PAGE 9: ... From the methods returned, recommendations were selected at random. In Table6 , the results of evaluating ASSOC with a context-recommendation system is presented. While NEXT has the best performance in predicting explore transitions, ASSOC still results in higher overall inter-class transitions hits.... ..."
Table 1: Iterative methods for the projection sys- tem.
"... In PAGE 5: ... This accuracy is su cient for the purpose of animating ames. Values in Table1 are the arithmetical means of the results.... In PAGE 5: ...Table 1: Iterative methods for the projection sys- tem. Table1 points out that the CGSSOR is the fastest ba- sic methods for solving the projection step. We must no- tice that after 100 iteration steps GS is far from conver- gence since the corresponding mean accuracy is equal to 0:22081146.... In PAGE 5: ... We determined its other parameters experimentally: the num- ber of grid levels have been set to three; three pre-smoothing iterations and three post-smoothing iterations on each inter- mediate grid have been used; nally the number of iterations on the coarsest grid was set to three. Table1 shows that the multigrid method is less e cient than CGSSOR method, both in term of computation time and accuracy. This can also be noted in Table 2 which summarizes the results of these methods used for solving the di usion step.... ..."
Cited by 2
Table 1. Number of states in minimal DFA for each Tomita language, and average number of fitness evaluations required by each sys- tem to learn the training set.
"... In PAGE 4: ... Smart shows results where we also fixed the maximum number of states to be 10, whereas for nSmart we set the number of states to be exactly the number of states in the minimal DFA consis- tent with the training set. Table1 summarizes these results, showing the average number of fitness evaluations needed by each system, together with the Genetic Programming ( GP ) system of Luke et al [11]. Note that in all cases the simple random hill-climber requires far fewer fitness evalu- ation than the GP method, and that in all cases apart from language 1, the Smart version requires far fewer than the plain one.... In PAGE 5: ...entations running on a 2.4GHz Pentium. Note that the smart hill climber significantly outperforms the plain hill- climber. This is partially explained by the fewer number of fitness evaluations required (see Table1 ), and partially by the reduced book-keeping, since in the smart hill-climber we replace the copy/mutate operation with an in-place mu- tation. 1 0 1 0 0 1 0 0 1 Figure 2.... ..."
Table 1 shows the quantitative evaluation of the sys- tem performance by breaking the simulation into different processes. The time consumption of the quasi-static simu- lation and rendering is relatively small compared to that of the dynamic simulation. For the expression generated by the contractions of more muscles such as sadness, although the number of dynamic nodes increases, the framerate can still reach about 21 fps. We can clearly verify that the pro- posed adaptive simulation algorithm of section 4 gracefully accelerates the simulation speed as a function of C6 CS and
"... In PAGE 7: ...562 2208 3603 21.6 25.0 7.6 5.0 26.6 Table1 : System performance. Notation: number of dy-... ..."
Table 1. Heterogeneities of the simulated sys- tems. a7 and a8 a66a7 are in MFLOPS. a20 and a8 a20
"... In PAGE 6: ... The second set of simulations studies the impact of con- trol and coordination overheads. These simulations were based on the dynamic 64-node HH LAN and WAN systems in Table1 .... In PAGE 6: ... Due to space limitation, the results are omitted here. The third set of results was extracted from the simulation of a 50-node dynamic HL system shown in Table1 . Block size adaptation occurred multiple times during the simula- tion.... In PAGE 7: ... Besides modeling the performance of the re- sources as normally distributed random variables, we also evaluated the performance in a scenario where the perfor- mance of some resources drops severely. A system similar to the 9-node HH system in Table1 was simulated. During the simulations, the performance of the most powerful node was altered such that it loses 75% of its compute power immediately after the computation begins.... ..."
Table 1: Percentage of correct documents retrieved in rst place of \disambiguation errors quot; in the collection. The only disadvantage is the small size of the collection, which does not allow ne-grained distinctions in the results. However, it has proved large enough to give meaningful statistics for the experiments reported here.Although designed for our concrete text retrieval testing purposes, the resulting database could also be useful for many other tasks. For instance, it could be used to evaluate automatic summarization sys- tems (measuring the semantic relation between the manually written and hand-tagged summaries of IR- Semcor and the output of text summarization sys- tems) and other related tasks.
1998
Cited by 98
Table 1: Physical layer parameters of the MIMO-OFDM sys- tems used for the performance evaluation.
2006
Cited by 1
Table 3: Comparison of word error rates (%) for sys- tems with different numbers of parameters
"... In PAGE 6: ... We trained the models using both our previous training algorithm and the GMS algorithm. Table3 shows that the GMS algorithm performs similarly to the old method for the 991 Genone model, but is significantly better for the 2027 Genone model, where the number of param- eters is very large relative to the amount of training data. This shows the robustness of the GMS algorithm relative to our previous approach.... ..."
Table 2. Qualitative Regression Equation Forms Coincident. The relationship between the response and explanatory variables stays the same for all states of W. In other words, the equations for all states are co- incident. This in fact is the situation for a static sys- tem environment assumed by the static query sampling method.
2000
"... In PAGE 4: ... The relationship between the response and explanatory variables may differ in the intercept term but not the slope terms for different states of W. The relevant equation in Table2 shows that the intercept term for the jth state of the qualitative variable is B0 0 + Bj 0 (j = 1; 2; ; m; and Bm 0 = 0). Since the slope terms remain the same for all states, the equations for different states are parallel.... In PAGE 4: ... The relationship between the response and explanatory variables may differ in the slope terms but not the intercept term for different states of W. The relevant equation in Table2 shows that the ith slope term (i = 1; 2; ; n) for the jth state of the qualitative variable is B0 i + Bj i (j = 1; 2; ; m; and Bm i = 0). The equations for different states have the same inter- cept term.... In PAGE 7: ... PROPOSITION 4.1 For the general qualitative regression cost model in Table2 with n quantitative explanatory vari- ables and one qualitative variable for m states, at least 10 (m (n + 1) + 1) observations need to be sampled. PROOF.... ..."
Cited by 6
Table 8:Coarse-grained evaluation of unsupervised S3 sys- tems for nouns (sorted by recall(%)). Our system given in bold.
2006
"... In PAGE 7: ... We also evaluated the systems on the coarse-grained sense groups provided by the Senseval-3 organisers. The results in Table8 show that our system is comparatively better on this coarse-grained disambiguation task. 6 Conclusions and Future Work We automatically acquired English sense exam- ples for WSD using large Chinese corpora and MT software.... ..."
Cited by 1
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