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We further use a topic model to discover emerging areas of the predicted technology convergence.
We use a topic model called Latent Dirichlet Allocation (LDA) first described by Blei et al. (Blei et al. [2003]).
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Our proposed method relies on a gene-specific text corpus and extracts commonalities between documents in an unsupervised manner using a topic model approach.
The first layer of phishGILLNET (phishGILLNET1) employs PLSA to build a topic model and uses a topic level similarity function for classification.
Romsaiyud [148] implemented a topic modeling algorithm and compared experimental results using both SAMOA and MOA.
Rodriguez et al. [23] propose to model various crowd behavior (or motion) modalities at different locations of the scene by using a Correlated Topic Model (CTM).
The first stage of the algorithm uses a PLSA topic model to provide regularized dimensionality reduction of its vector quantized input features, feeding the resultant posterior topic probabilities into individual-patch-level Logistic Regression Classifiers (LRCs).
Then, we use the topic model described in Fig. 4 to analyze the underlying topics in a document collection.
Most of the studies concentrate on proposing more elaborated features to represent documents in the vector space model, including the use of topic model techniques, such as LSI and LDA, to obtain latent semantic features.
This study is closely related to Bergholz et al. [16, 17], in the sense that, we use topic model PLSA (as compared to CLTOM) for phishing detection.
In our method, we used the topic model implementation in the R ' lda' package [ http://cran.r-project.org/web/packages/lda/] implementing the classic LDA approach suggested by Blei et al [ 4].
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