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The similarity between two documents is computed based on the distance of their representative vectors.

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Once the vector representations of all documents were computed, the association between two genes, k and l, was computed as follows: (2) a s s o c i a t i o n k l = ∑ i = 1 N W i k * W i l where k = 1 … m and l = 1.

The document decryption is computed as (DEC _{_{2}}left( D_{i}right)) which represents decryption of document (D_{i}) with K_{2}}left).  .

The document encryption is computed as (ENC_{K_{2}}left( D_{i}right)) which represents encryption of document (D_{i}) with key (K_{2}).

For each query document, its Hamming distance compared to all other documents in the data is computed and the top D similar documents are retrieved.

For each document identified, a relevancy metric (specifically, binary term occurrence) is computed to evaluate whether the document is truly related and not just a copy of the same document or too distinct to be useful.

Normally, the mixture is computed at the whole-document level, that is, the entire document contains material on several topics, without specifying where they occur in the document.

Average document length

In the training, each member in the matrix kernel is computed by applying the MLRBF function over all document vectors.

SES is computed daily.

In the M-step, word-topic and topic-document probabilities are computed using expressions given in PLSA model section.

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