compared with
Current by laura.tolosi
on Jun 03, 2015 17:09.

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* We are currently using: stopwords elimination, stemming and a bigram model for feature extraction
* The multi-label classification is achieved by training K independent classifiers (perceptron, sigmoid perceptrons), corresponding to the K possible labels. For each classifier, the interpretation is: what is the likelihood that sample x has label l, against the alternative that it does not? After training all K classifiers, for each sample, the top highest likelihoods give the set of labels. A rule of thumb is used for deciding how many labels should be returned.