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Forming training set for Multinomial Naive Bayes
Is it true that Multinomial Naive Bayes requires equally by count training data for each class to get best performance?
For example, we forming classifier for three classes - Japan, China, Korea.
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Combining labeled and unlabeled data for training
When training my algorithm, if I can get some i.e. data my future test data that has no labels can it improve my algorithm's efficiency, is there any mathematical proof for it?
PS: I think ...
