# What loss function can I use for linear classification?

I have a question about loss function in bayes classification. Let see similar case of loss function in linear classification: Given data $(x,y)=${$(x_1,y1)....(x_n,y_n)$} is map to label $T=${-1,1}. The loss function of that method is given by $$L=\sum(y-T)^2$$ How about if I use bayes classification,which is loss function of that method?Thank you so much http://en.wikipedia.org/wiki/Naive_Bayes_classifier

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So what is the loss function you use?. The likelihood of the data for the given model. Please refer to the Wikipedia site on Naive Bayes for several examples. It is a measure of likely it is, that the data has been generated by the given model. The canonical form given in that site for the likelihood (for a given sample $(F_{1}, ... , F_{n})$) is,
$$L = P(C)\prod_{i}P(F_{i}|C)$$
This is of course a minor comment, but technically the loss function is not "the likelihood of the data for the given model" but the "negative likelihood of the model parameters for the given data" (minimizing loss = maximizing likelihood). – Amelio Vazquez-Reina Jul 15 '14 at 15:58