diff --git a/modules/ml/doc/normal_bayes_classifier.rst b/modules/ml/doc/normal_bayes_classifier.rst
index 90e874b212..646c05d55f 100644
--- a/modules/ml/doc/normal_bayes_classifier.rst
+++ b/modules/ml/doc/normal_bayes_classifier.rst
@@ -13,53 +13,44 @@ CvNormalBayesClassifier
 -----------------------
 .. ocv:class:: CvNormalBayesClassifier
 
-Bayes classifier for normally distributed data. ::
+Bayes classifier for normally distributed data.
 
-    class CvNormalBayesClassifier : public CvStatModel
-    {
-    public:
-        CvNormalBayesClassifier();
-        virtual ~CvNormalBayesClassifier();
+CvNormalBayesClassifier::CvNormalBayesClassifier
+------------------------------------------------
+Default and training constructors.
 
-        CvNormalBayesClassifier( const Mat& _train_data, const Mat& _responses,
-            const Mat& _var_idx=Mat(), const Mat& _sample_idx=Mat() );
+.. ocv:function:: CvNormalBayesClassifier::CvNormalBayesClassifier()
 
-        virtual bool train( const Mat& _train_data, const Mat& _responses,
-            const Mat& _var_idx=Mat(), const Mat& _sample_idx=Mat(), bool update=false );
+.. ocv:function:: CvNormalBayesClassifier::CvNormalBayesClassifier( const Mat& trainData, const Mat& responses, const Mat& varIdx=Mat(), const Mat& sampleIdx=Mat() )
 
-        virtual float predict( const Mat& _samples, Mat* results=0 ) const;
-        virtual void clear();
-
-        virtual void save( const char* filename, const char* name=0 );
-        virtual void load( const char* filename, const char* name=0 );
-
-        virtual void write( CvFileStorage* storage, const char* name );
-        virtual void read( CvFileStorage* storage, CvFileNode* node );
-    protected:
-        ...
-    };
+.. ocv:cfunction:: CvNormalBayesClassifier::CvNormalBayesClassifier( const CvMat* trainData, const CvMat* responses, const CvMat* varIdx=0, const CvMat* sampleIdx=0 )
 
+The constructors follow conventions of :ocv:func:`CvStatModel::CvStatModel`. See :ocv:func:`CvStatModel::train` for parameters descriptions.
 
 CvNormalBayesClassifier::train
 ------------------------------
 Trains the model.
 
-.. ocv:function:: bool CvNormalBayesClassifier::train(  const Mat& _train_data,  const Mat& _responses,                 const Mat& _var_idx =Mat(),  const Mat& _sample_idx=Mat(),  bool update=false )
+.. ocv:function:: bool CvNormalBayesClassifier::train( const Mat& trainData, const Mat& responses, const Mat& varIdx = Mat(), const Mat& sampleIdx=Mat(), bool update=false )
 
-The method trains the Normal Bayes classifier. It follows the conventions of the generic ``train`` approach with the following limitations: 
+.. ocv:cfunction:: bool CvNormalBayesClassifier::train( const CvMat* trainData, const CvMat* responses, const CvMat* varIdx = 0, const CvMat* sampleIdx=0, bool update=false )
+
+    :param update: Identifies whether the model should be trained from scratch (``update=false``) or should be updated using the new training data (``update=true``).
+
+The method trains the Normal Bayes classifier. It follows the conventions of the generic :ocv:func:`CvStatModel::train` approach with the following limitations: 
 
 * Only ``CV_ROW_SAMPLE`` data layout is supported.
 * Input variables are all ordered.
-* Output variable is categorical , which means that elements of ``_responses`` must be integer numbers, though the vector may have the ``CV_32FC1`` type.
+* Output variable is categorical , which means that elements of ``responses`` must be integer numbers, though the vector may have the ``CV_32FC1`` type.
 * 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`` ).
-
 CvNormalBayesClassifier::predict
 --------------------------------
 Predicts the response for sample(s).
 
 .. ocv:function:: float CvNormalBayesClassifier::predict(  const Mat& samples,  Mat* results=0 ) const
 
-The method ``predict`` estimates the most probable classes for input vectors. Input vectors (one or more) are stored as rows of the matrix ``samples`` . In 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.
+.. ocv:cfunction:: float CvNormalBayesClassifier::predict( const CvMat* samples, CvMat* results=0 ) const
+
+The method estimates the most probable classes for input vectors. Input vectors (one or more) are stored as rows of the matrix ``samples``. In 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.