fixed many warnings from GCC 4.6.1

modules/ml/src/boost.cpp

@@ -1249,13 +1249,11 @@ CvBoost::update_weights( CvBoostTree* tree )
         // recent weak classifier we know the responses. For other samples we need to compute them
         if( have_subsample )
         {
-            float* values0, *values = (float*)cur_buf_pos;
+            float* values = (float*)cur_buf_pos;
             cur_buf_pos = (uchar*)(values + data->buf->step);
-            uchar* missing0, *missing = cur_buf_pos;
+            uchar* missing = cur_buf_pos;
             cur_buf_pos = missing + data->buf->step;
             CvMat _sample, _mask;
-            values0 = values;
-            missing0 = missing;
 
             // invert the subsample mask
             cvXorS( subsample_mask, cvScalar(1.), subsample_mask );

modules/ml/src/rtrees.cpp

@@ -697,28 +697,18 @@ float CvRTrees::predict( const CvMat* sample, const CvMat* missing ) const
 
 float CvRTrees::predict_prob( const CvMat* sample, const CvMat* missing) const
 {
-    double result = -1;
-    int k;
-	
 	if( nclasses == 2 ) //classification
     {
-        int max_nvotes = 0;
         cv::AutoBuffer<int> _votes(nclasses);
         int* votes = _votes;
         memset( votes, 0, sizeof(*votes)*nclasses );
-        for( k = 0; k < ntrees; k++ )
+        for( int k = 0; k < ntrees; k++ )
         {
             CvDTreeNode* predicted_node = trees[k]->predict( sample, missing );
-            int nvotes;
             int class_idx = predicted_node->class_idx;
             CV_Assert( 0 <= class_idx && class_idx < nclasses );
 			
-            nvotes = ++votes[class_idx];
-            if( nvotes > max_nvotes )
-            {
-                max_nvotes = nvotes;
-                result = predicted_node->value;
-            }
+            ++votes[class_idx];
         }
 		
 		return float(votes[1])/ntrees;

modules/ml/test/test_mltests2.cpp

@@ -391,7 +391,6 @@ float ann_calc_error( CvANN_MLP* ann, CvMLData* _data, map<int, int>& cls_map, i
     int cls_count = (int)cls_map.size();
     Mat output( 1, cls_count, CV_32FC1 );
     CvMat _output = CvMat(output);
-    map<int, int>::iterator b_it = cls_map.begin();
     for( int i = 0; i < sample_count; i++ )
     {
         CvMat sample;