Sentence examples for underlying inputs from inspiring English sources

When Big Data challenges are already present in any of the underlying inputs or outputs for Intrusion Detection, the overall system will likely experience Big Data challenges as well unless the Big Data bottleneck is eliminated.

Therefore, it is preferable to parametrize the input which is possible if certain knowledge about origin and processes underlying inputs is available.

As opposed to random initialization, we use the RSM as a pre-training stage, which learns topics that initially capture properties of the underlying input only.

Thus, the RSM is treated as a pre-training stage whose task is to learn initial weights that capture properties of the underlying input observations in an unsupervised manner.

Although obtained from different underlying input graphs, all three networks show very similar characteristics in terms of the semantic similarities between the linked processes.

Finally, there are a considerable number of processes for which the links to other processes vary with the underlying input graph, indicating that the different linkage networks provide non-redundant information.

We show that this mechanism provides for the selective activation and phase locking of the cells in the network, underlying input-correlated spatio-temporal pattern formation, and could be the basis for reliable spike-timing dependent plasticity.

Extreme parameter values observed from field studies could only be reproduced by assuming genetic relationships between the underlying input traits.

The underlying Input-Output Hidden Markov Model learning can now accommodate dynamic input data for each time point in the following way.

Pooled groups are known to be often poorly represented in the underlying input data and may be missing data for key group components.

This has here been illustrated for the nine input parameters assumed to be most interesting for the mammalian circadian clock and the 16 state variables of the model, where the generated N-way metamodels allowed flexible quantitative input-output regressions yielding informative graphical insight into the main underlying input-output map characteristics.