However, the detection models built using clinical features have a number of limitations as mentioned below: The modeling process is localization or segmentation dependent.
Once the TDF matrix is built using components described above, all three layers of phishGILLNET employs PLSA to build the topic model for phishing detection.
Logistic regression was used to build the adjusted model.
Backward stepwise methods were used to build the multivariate model.
Ten-fold stratified cross-validation was used over the training dataset to build detection models with these classifiers.
Similarly, although traces of Nugache contain data of four malicious hosts, the dataset is very small (see Table 1) and thus not suitable to build detection models.
In target tracking, reliable trajectories which are obtained by incremental linking of detection responses frame by frame are used to build the appearance models for visual tracking.
These are the parameters necessary to build the model.
We selected the RBF Kernel to build the model.
We used a four-step approach to build the model.
