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@@ -17,7 +17,8 @@ Bayes](http://en.wikipedia.org/wiki/Naive_Bayes_classifier#Multinomial_naive_Bay
which is typically used for [document
classification](http://nlp.stanford.edu/IR-book/html/htmledition/naive-bayes-text-classification-1.html).
Within that context, each observation is a document and each
-feature represents a term whose value is the frequency of the term.
+feature represents a term whose value is the frequency of the term.
+Feature values must be nonnegative to represent term frequencies.
[Additive smoothing](http://en.wikipedia.org/wiki/Lidstone_smoothing) can be used by
setting the parameter $\lambda$ (default to $1.0$). For document classification, the input feature
vectors are usually sparse, and sparse vectors should be supplied as input to take advantage of