Suggestions(2)
Exact(3)
It employs the PLSA modeling technique (described in Section 4) to build the topic model.
In this study, TEM algorithm described earlier in Section 4 was employed to build the topic model.
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.
Similar(57)
In order to build the PLSA model, the training data are further split into 90% for building the topic model and 10% for computing perplexity, thus, independent datasets for training, computing perplexity, and testing.
The topic model represents each experiment as a distribution over topics.
For both views, once the PLSA model is built, the topic distribution probabilities are extracted as features.
The first layer of phishGILLNET (phishGILLNET1) employs PLSA to build a topic model and uses a topic level similarity function for classification.
In order to build PLSA topic model, which all three layers of phishGILLNET employs, the methodology requires preparation of Term Document Frequency (TDF) matrix.
The first layer (phishGILLNET1) employs Probabilistic Latent Semantic Analysis (PLSA) to build a topic model.
All three layers of phishGILLNET employ PLSA (see Section 4) to build a topic model that discovers phishing topics and non-phishing topics.
To build our topic models, we used the Mallet toolkit [ 16] and to train our logistic regression models we used the LIBLINEAR toolkit [ 3, 4].
Write better and faster with AI suggestions while staying true to your unique style.
Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com