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https://issues.apache.org/jira/browse/LUCENE-4345?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13447243#comment-13447243
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Tommaso Teofili commented on LUCENE-4345:
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bq. So we could consider performance-driven heuristics/approximations like
MoreLikeThis does based on things like local term frequency within the
document/term length, whatever to save on docFreq() calls, if it makes sense (i
have to look at the formula in more detail here).
The generic formula is _C = argmax( P(doc|class) * P(class) )_ , I agree it
makes sense to incrementally see if we can find good heuristics /
approximations which low the computational cost of this calculation.
bq. the current code, given a word that appears many times in the document,
will do many computations when instead we could really just work across the
unique terms within the document.
another good point where we can improve, thanks :)
I managed to remove all the Maps from the code, I'll attach the patch shortly.
I'll then work on removing the tokenizeDoc() loop.
> Create a Classification module
> ------------------------------
>
> Key: LUCENE-4345
> URL: https://issues.apache.org/jira/browse/LUCENE-4345
> Project: Lucene - Core
> Issue Type: New Feature
> Reporter: Tommaso Teofili
> Assignee: Tommaso Teofili
> Priority: Minor
> Attachments: LUCENE-4345.patch, SOLR-3700_2.patch, SOLR-3700.patch
>
>
> Lucene/Solr can host huge sets of documents containing lots of information in
> fields so that these can be used as training examples (w/ features) in order
> to very quickly create classifiers algorithms to use on new documents and /
> or to provide an additional service.
> So the idea is to create a contrib module (called 'classification') to host a
> ClassificationComponent that will use already seen data (the indexed
> documents / fields) to classify new documents / text fragments.
> The first version will contain a (simplistic) Lucene based Naive Bayes
> classifier but more implementations should be added in the future.
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