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Equation (1) is a weighted mixture of word distributions called aspect model [14].
In (1),,, are the word distributions and,, are the weights of distributions.
Then, the model fitting involves the estimation of topic specific visual word distributions and image specific latent topic distributions from the given database using Maximum Likelihood Estimation (MLE).
This statistical regularity in word distributions is found across a wide range of languages [16], [18].
The limited data that we present from the GENIA corpus is primarily for comparison of word distributions in the two corpora all data on alternations in the paper is from the PennBioIE corpus.
The D2z score is an alignment-free sequence comparison metric which compares k-mer word distributions between two sequences [ 37].
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Its just that the meaning of the word "distribution" in the digital age has shifted.
Step 1 The word distribution of the kth topic, (phi _{k}), is generated from a Dirichlet distribution with parameter ({varvec{beta }}).
Using labeled emails as input dataset, containing phishing emails and non-phishing emails, topic distribution probabilities and word distribution probabilities are obtained by building a PLSA model.
However, the state-of-the-art works typically utilize bag-of-visual words (BoVW) model which only focuses on the word distribution of STIPs and ignore the distinctive character of word structure.
This model identified sub-events based on the burstiness of the input data stream and the word distribution of the main event.
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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