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The ANN models are now constructed using the training data sets generated with the help of DOE techniques in "Fractional factorial design (second phase of DOE)" section.
The CEPB is constructed using the training images and then located at both the transmitter and receiver sides of the communication system.
It is constructed using the training data to estimate the probability of each class given the feature vectors of a new instance.
The model constructed using the training set was then further validated on SEER sub-cohorts as well as patients from the MBRCC and the Director's Challenge cohorts [21], without re-estimating parameters of the model or cutoffs.
To strengthen our findings, ARMA models of order (2,3), (1,3), (1,1), (2,1), (1,2) and (1,1) (the figures indicate autoregressive and moving average terms) were constructed using the training set 36-month data to provide adequate model fit for monthly ESKAPE infections due to E. faecium, S. aureus, K. pneumoniae, A. baumannii, P. aeruginosa and Enterobacter species, respectively.
The classifier (constructed using the training set as shown in Table 2) was tested on a dataset of 116 conotoxin sequences obtained from Mondal et al 24 and the predictions made by the scheme were compared with the known classes of the sequences in the study by Mondal et al. 24 The scheme gave an accuracy of 93.1% for the test set and the confusion matrix is presented in Figure 5B.
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Though somewhat less evident, the same is true for the model constructed using the biased training set R (Fig. 4F vs 4C).
All the above classifiers have been constructed using the same training dataset, PDNA-62, which shares less than 25% sequence identity with the PDC25t dataset.
In order to compare the performance of the sparse learning techniques, classical regression method including OLR and logistic regression with backward stepwise selection (LR-BS) were also constructed using the same training dataset.
Soft computing approaches such as connectionism, support vector machine, hidden Markov model [23, 24, 26], etc., usually require complete training data as the performance of the model highly depends on the decision boundary constructed using the information from the training examples.
Each of the tested timetables is constructed using the Passenger Centric Train Timetabling Problem (PCTTP), that is solved using a simulated annealing heuristic.
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