Minor doc fix

doc/check_docs_whitelist.txt

@@ -43,7 +43,8 @@ Mat cv::findFundamentalMat( InputArray points1, InputArray points2, OutputArray
 Mat findHomography( InputArray srcPoints, InputArray dstPoints, OutputArray mask, int method=0, double ransacReprojThreshold=3);
 
 ########################################## ml ######################################
-
+cvParamLattice
+cvDefaultParamLattice
 CvBoostTree
 CvForestTree
 CvSVMKernel

modules/ml/doc/random_trees.rst

@@ -223,7 +223,7 @@ CvRTrees::calc_error
 The method is identical to :ocv:func:`CvDTree::calc_error` but uses the random forest as predictor.
 
 
-.. index:: CvRTrees::get_train_error()
+.. index:: CvRTrees::get_train_error
 
 .. _CvRTrees::get_train_error:
 
@@ -236,7 +236,7 @@ CvRTrees::get_train_error
 The method works for classification problems only. It returns the proportion of incorrectly classified train samples.
 
 
-.. index:: CvRTrees::get_rng()
+.. index:: CvRTrees::get_rng
 
 .. _CvRTrees::get_rng:
 

modules/ml/doc/statistical_models.rst

@@ -142,7 +142,7 @@ The method ``write`` stores the complete model state in the file storage with th
 
 CvStatModel::read
 -----------------
-.. ocv:function:: void CvStatMode::read( CvFileStorage* storage, CvFileNode* node )
+.. ocv:function:: void CvStatModel::read( CvFileStorage* storage, CvFileNode* node )
 
     Reads the model from the file storage.
 
@@ -157,7 +157,7 @@ The previous model state is cleared by ``clear()`` .
 
 CvStatModel::train
 ------------------
-.. ocv:function:: bool CvStatMode::train( const Mat& train_data, [int tflag,] ..., const Mat& responses, ...,     [const Mat& var_idx,] ..., [const Mat& sample_idx,] ...     [const Mat& var_type,] ..., [const Mat& missing_mask,] <misc_training_alg_params> ... )
+.. ocv:function:: bool CvStatModel::train( const Mat& train_data, [int tflag,] ..., const Mat& responses, ...,     [const Mat& var_idx,] ..., [const Mat& sample_idx,] ...     [const Mat& var_type,] ..., [const Mat& missing_mask,] <misc_training_alg_params> ... )
 
     Trains the model.