This is a simple classification model assuming that feature vectors from each class are normally distributed (though, not necessarily independently distributed), so the whole data distribution function is assumed to be a Gaussian mixture, one component per class. Using the training data the algorithm estimates mean vectors and covariance matrices for every class, and then it uses them for prediction.
**[Fukunaga90] K. Fukunaga. Introduction to Statistical Pattern Recognition. second ed., New York: Academic Press, 1990.**
The method trains the Normal Bayes classifier. It follows the conventions of the generic ``train`` "method" with the following limitations: only CV_ROW_SAMPLE data layout is supported; the input variables are all ordered; the output variable is categorical (i.e. elements of ``_responses`` must be integer numbers, though the vector may have ``CV_32FC1`` type), and missing measurements are not supported.
In addition, there is an ``update`` flag that identifies whether the model should be trained from scratch ( ``update=false`` ) or should be updated using the new training data ( ``update=true`` ).
The method ``predict`` estimates the most probable classes for the input vectors. The input vectors (one or more) are stored as rows of the matrix ``samples`` . In the case of multiple input vectors, there should be one output vector ``results`` . The predicted class for a single input vector is returned by the method.
