diff --git a/Classification/doc/Classification/Classification.txt b/Classification/doc/Classification/Classification.txt
index d3ee5df6ca7..0364899fd85 100644
--- a/Classification/doc/Classification/Classification.txt
+++ b/Classification/doc/Classification/Classification.txt
@@ -10,7 +10,7 @@ This component implements a generalization of the algorithm described in \cgalCi
 
 \section Classification_Organization Package Organization
 
-Classification of point sets is achieved as follows:
+%Classification of point sets is achieved as follows:
 
 - some analysis is performed on the input point set;
 - attributes are computed based on this analysis;
@@ -29,11 +29,11 @@ Organization of the package.
 
 \subsection Classification_analysis Analysis
 
-Classification is based on the computation of local attributes of points. These attributes often require precomputed analysis structures: such data structures might be shared by several attributes and are therefore computed separately.
+%Classification is based on the computation of local attributes of points. These attributes often require precomputed analysis structures: such data structures might be shared by several attributes and are therefore computed separately.
 
 \cgal provides the following structures:
 
-- `CGAL::Classification::Neighborhood` stores spatial searching structures and provides adapted queries on indexed points;
+- `CGAL::Classification::Point_set_neighborhood` stores spatial searching structures and provides adapted queries for points;
 - `CGAL::Classification::Local_eigen_analysis` precomputes covariance matrices on local neighborhood of points and stores the associated eigenvectors and eigenvalues;
 - `CGAL::Classification::Planimetric_grid` is a 2D grid used for digital terrain modeling.
 
@@ -99,7 +99,7 @@ The following code snippet shows how to add types to the classification object a
 
 \subsection Classification_regularization Regularization
 
-Classification is performed by minizing an energy over the input point set. This energy can be regularized with different methods. \cgal provides 3 different methods for classification, ranging from high speed / low quality to low speed / high quality:
+%Classification is performed by minizing an energy over the input point set. This energy can be regularized with different methods. \cgal provides 3 different methods for classification, ranging from high speed / low quality to low speed / high quality:
 
 - `CGAL::Point_set_classification::run()` 
 - `CGAL::Point_set_classification::run_with_local_smoothing()`
@@ -213,7 +213,7 @@ The classification algorithm is designed to be as flexible as possible: the user
 
 \section Classification_training Training
 
-Classification is based on relationships between attributes and types. Each attribute has a specific weight and each pair of attribute/type has a specific effect. This means that the number of parameters to set up becomes rapidly large: if 6 attributes are used to classify between 4 classification types, 30 parameters have to be set up.
+%Classification is based on relationships between attributes and types. Each attribute has a specific weight and each pair of attribute/type has a specific effect. This means that the number of parameters to set up becomes rapidly large: if 6 attributes are used to classify between 4 classification types, 30 parameters have to be set up.
 
 Though it is possible to set them one by one, \cgal also provides a training algorithm that requires a small set of ground truth points provided by the user. More specifically, the user must provide, for each classification type he/she wants to classify, a set of known inliers among the input point set (for example, selecting one roof, one tree, one section of the ground). The training algorithm works as follows: