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While the second and fourth ranked methods utilized features in addition to topic features, phishGILLNET utilized exclusively topic features.
The principle of topic features is shown in Figure 3. Figure 3 The principle of topic features.
phishGILNET2 builds a finer mesh utilizing PLSA topic features and AdaBoost (see Section 5).
phishGILLNET2 employs AdaBoost using PLSA topic features and builds a better classifier than phishGILLNET1.
Thus, topic features using AdaBoost are robust for 3-class classification.
Therefore, topic features can be divided into several levels according to their position in documents.
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More on this topic: Feature: what's wrong with Australian cinema?
A true and nuanced conversation about the topic featuring someone who uses the term daily might have revealed a history of empowerment through the community and the protection of a self-made family.
Partly, this might be because, as usual with high-level policy debates, there aren't many women in the conversation – a recent UN meeting I went to on the topic featured panels composed entirely of men.
3 Temporal topic feature characterization Validating the frequency of each research topic in the text corpus and generate a feature vector for each topic.
According to the analysis in Section 2, topic feature set has parlous lower dimension and features in it have higher discrimination.
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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