### Table 8: The communication function @H(a) = a if a = 2 H (D1)

### Table 2: Recognition rates of multiple neural networks (%). Una and Maj stands for unanimity and majority, respectively. Subject

"... In PAGE 13: ... However, if the number of the di erent neural networks would be increased, the state of the results might be reversed. Table2 reports the aggregate of the recognition rates. Figure 5 illustrates the error rates of the multiple network scheme as compared with each network.... ..."

### Table 3: Pair-based performance of Bidirectional Recursive Neural Networks trained with multiple alignment profiles.

2004

"... In PAGE 7: ... One additional index we use is Q2, which estimates the probability of correct prediction at the level of individual pairs of bonded cysteines, either in contact or not. Table3 shows the results of this kind of analysis. We report performance grouped according to the topology class and the number of disulfide bonds of each chain.... ..."

Cited by 26

### Table 3: Pair-based performance of Bidirectional Recursive Neural Networks trained with multiple alignment profiles.

2004

"... In PAGE 7: ... One additional index we use is Q2, which estimates the probability of correct prediction at the level of individual pairs of bonded cysteines, either in contact or not. Table3 shows the results of this kind of analysis. We report performance grouped according to the topology class and the number of disulfide bonds of each chain.... ..."

Cited by 26

### Table 1: Neural network estimation results

"... In PAGE 8: ... The decision whether to use the neural net estimation or the analysis tool results can be based on a cost function re ecting the required delity and criticality of the results. Table1 shows four test results of the neural network for the aerodynamic analysis tool. Best results were obtained when the training was done for 1000 cycles with the struc- ture shown in Figure 5 and the learning rate was set to 0.... ..."

### Table 7. Estimation Results of the Neural Network

"... In PAGE 25: ...5. The estimation results of each model are shown in Table7 . In this verification, the data were not adopted as estimation objects, except for those data that were evaluated as the highest and lowest within a set of visual objects.... ..."

### TABLE I: Specification of the measurements used for the neural networks.

### Table 2. Neural network modeling

2006

"... In PAGE 5: ... In model A, we calculated the IBIs from the steady-state solution, whereas in model B we continuously solved the network without discarding the transients. The model param- eters are summarized in Table2 . The parameters were the same as previously described (4), except for TNI1 (see Table 2).... ..."

### Table 3: Linear, Polynomial, and Neural Network Estimation of Brazilian Stock Price Adjustment

"... In PAGE 5: ...II. Linear, Polynomial and Neural Network Approximation: Brazil The results of the three alternative methods, for linear estimation, for polynomial approximation of degree 1 (equivalent to linear estimation) through 10, and for feedforward neural network estimation, with one through ten hidden neurons, appears in Table3 , below. Table 3: Linear, Polynomial, and Neural Network Estimation of Brazilian Stock Price Adjustment... In PAGE 6: ... The reason is that the logsigmoid function converges quickly to one at values greater than three or four , and to zero at values less that minus three or minus four. Thus, in the absence of a squasher, the partial derivatives of units greater than three or four in absolute value will be close to zero, The results of Table3 show that the best neural network, in terms if the Hannan-Quinn criterion, discussed in Chapter II, is the feedforward network with seven neurons. This model beats the simple linear model and the best polynomial approximation ( of degree 9) not only with in-sample performance, with a multiple correlation coefficient or R 2 of 17 percent, but also with out-of-sample performance, with a root mean squared error value of .... ..."

### Table 3: Results of the best neural network and multiple regression models

2005

"... In PAGE 26: ... The views elaboration of system is supported by the use cases description technique [9][7]. This technique consists in describing the use case as an action sequence that will make an agent to achieve his goal ( Table3 ) [4]. An action is an effect produced by an agent according the given way on the system or by the system itself.... In PAGE 27: ...Journal of information and organizational sciences, Volume 29, Number 2 (2005) 19 Table3 : Use cases decomposition in actions Agents Use case Decomposition in actions Loan a1: To identify an adherent a2: Count adherent (To check right loan for member) a3: To seek for an exemplary a4: To treat an exemplary (to validate the output of an exemplary) a5: To treat adherent (to indicate the loan by adherent) Reservation a1: To identify an adherent a2: Count adherent (To check right loan for member) a3: To seek for an exemplary a6: To reserve an exemplary Restitution a1: To identify adherent a3: To seek for an exemplary a4: To treat an exemplary (to validate the input of exemplary ) a5: To treat adherent (to indicate the return of an exemplary) Loan if counts blocked a1: To identify adherent a2: Count adherent (To check right loan for member) Librarian Identification member a1: To identify adherent New adherent a2: Adherent account (to Add adherent) Adherents responsible Litigation a1: To identify adherent a2: Count adherent (Blocked count adherent) a7: To inform adherent Exemplary addition a3: To seek for an exemplary a8: To add copy Exemplaries responsible Exemplary withdrawal a3: To seek exemplary a9: To withdraw the damage exemplary Figure 4: Viewpoint diagrams of MEDIA LIBRARY system After, the Analysis Profile must enable the analyst to acquire scenarios as collaboration diagrams for each use case. The Collaboration diagrams concentrates on the structure of interaction between objects and their inter-relationships rather than focuses the temporal ... In PAGE 72: ...f the matching algorithms. Its function was in the evaluation of match results. This data set was set up especially to evaluate the field-matching effectiveness of the various algorithms irrespective of other constraints such as field weights. The results from running the three algorithms on this data set are presented in Table3 . Figures 3 and 4 depict the percentage precision and percentage recall achieved for varying thresholds respectively.... In PAGE 73: ...47%. Table3 : Experimental Results on Test data 2 Positional Algorithm Recursive Algorithm with word-base Recursive Algorithm with character-base Threshold %Recall %Precision %Recall %Precision %Recall %Precision 0.... ..."