In 1899 David Hilbert published a book in which he constructed such models, using exactly the method of interpretation that we have just described.
These sub-data sets were then used to compute two sub-models using exactly the same procedure and parameters as when using the full data set.
It does demonstrate, however, the general continuity in results between the two versions of the model, when using exactly the same input data, as well as evidence of deliberately introduced changes in response to some of the shortcomings identified in InterVA-3.
The RMSEs of SVM models developed using exactly the same descriptors as those used in MLRA models were on average 7 ± 1 °C lower than the RMSEs of the MLRA models.
To do a fair comparison between our previous CRF model and this CNF model, we used exactly same data to train both CRF and CNF models.
We constructed RF models on the TR299 dataset using exactly the same scheme as the original RF models and applied the models to predict putative DNA-binding residues in the test dataset TS75 whose SS information was predicted by the PREDATOR program (Frishman and Argos, 1997).
Here, we also trained other RF models on the TR299 dataset using exactly the same strategy as the original RF models, except that the training data instances were extracted according to the criterion of a cutoff distance of 4.5 Å in the definition of a binding residue.
Consequently, the analysis was re-run, using exactly the same model, but changing the 'base category' of the setting variable, to care homes.
New models should be tested by comparing them with well-known ones, using exactly the same tools.
In this paper, we extend our previous work from [31, 32] in two specific ways: (i) we extend the network source model used from exactly sparse to near-sparse signals, and (ii) we provide a detailed mathematical and numerical justification of the usage of sparse recovery algorithms (including a bound on the reconstruction error) for this source model.
