I\'m trying to implement the naive Bayes classifier for sentiment analysis. I plan to use the TF-IDF weighting measure. I\'m just a little stuck 开发者_如何转开发now. NB generally uses the word(featur
My site is getting larger and it\'s starting to attract a lot of spam through various channels.The site has a lot of different types of UGC (profiles, forums, blog comments, status updates, private me
I\'m very sorry if I\'m wording this wrong in advance but I have a large dataset and I am trying to analyze it, but most of the data is not correct and need some help figuring out how to select the co
My requirement is probably close to what one expects of an \"Expert System\". And looking for the simplest solution, that can give me real-time or near-real time inference, with some offline (non-real
I am implementing Naive Bayes algorithm for text classification. I have ~1000 documents for training and 400 documents for testing. I think I\'ve implemented training part correctly, but I am confused
Let\'s say I have a bunch of essays (thousands) that I want to tag, categorize, etc.Ideally, I\'d like to train something by manually categorizing/tagging a few hundred, and then let the thing loose.
It is said that different algorithms have different parameters. I don\'t really see this as true, say if it is a tree decision algorithm and naive bayesian algorithm, what开发者_运维知识库 is the para
I\'m wondering if a Bayes classifier makes sense for an application where the same phrase \"served cold\" (for example) is \"good\" when associated some things (beer, soda) but \"bad\" when related to
Would like to analyze a stream of events, sharing certain characteristics (s.a. a common source), and within a given time-window, ultimately to correlate those multiple events and draw some inference
In my application(C#) i need to filter emails based on their content. If an email is a double-op开发者_如何学Ct in need to send it to a specified email address if it\'s a normal email i should send it
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