Update documentation

docs/user/features.rst

@@ -54,8 +54,8 @@ Ability to directly process missing data
 Learning algorithms for the general fuzzy min-max neural network supported by the library may classify inputs with missing
 data directly without the need for replacing or imputing missing values as in other classifiers.
 
-Continual learning of new classes in an incremental manner
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Continual learning ability of new classes
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 Incremental learning algorithms of hyperbox-based models in the **hyperbox-brain** library can grow and accommodate new
 classes of data without retraining the whole classifier. Incremental learning algorithms themselves can generate new hyperboxes
 to represent clusters of new data with potentially new class labels both in the middle of normal training procedure and in the

index.html

@@ -139,7 +139,7 @@
 <div class="keyfeatures-box-content keyfeatures-underline">
 	<p></p>
 	<div class="keyfeatures-box-title">Learning from both labelled and unlabelled data</div>
-	<div class="keyfeatures-box-text">One of the exciting features of learning algorithms for the general fuzzy min-max neural network(GFMMNN) is the capability of creating classification boundaries among known classes and clustering data and representing them as hyperboxes in the case that labels are not available. Unlabelled hyperboxes is then possible to be labelled on the basis of the evidence of next incoming input samples. As a result, the GFMMNN models have the ability to learn from the mixed labelled and unlabelled datasets in a native way.
+	<div class="keyfeatures-box-text">One of the exciting features of learning algorithms for the general fuzzy min-max neural network (GFMMNN) is the capability of creating classification boundaries among known classes and clustering data and representing them as hyperboxes in the case that labels are not available. Unlabelled hyperboxes is then possible to be labelled on the basis of the evidence of next incoming input samples. As a result, the GFMMNN models have the ability to learn from the mixed labelled and unlabelled datasets in a native way.
 	</div>
 	<p></p>
 </div>
@@ -153,7 +153,7 @@
 <div class="keyfeatures-box-content keyfeatures-underline">
 	<p></p>
 	<div class="keyfeatures-box-title">Continual learning of new classes in an incremental manner</div>
-	<div class="keyfeatures-box-text">Incremental learning algorithms of hyperbox-based models in the **hyperbox-brain** library can grow and accommodate new classes of data without retraining the whole classifier. Incremental learning algorithms themselves can generate new hyperboxes to represent clusters of new data with potentially new class labels both in the middle of normal training procedure and in the operating time where training has been finished. This property is a key feature for smart life-long learning systems.
+	<div class="keyfeatures-box-text">Incremental learning algorithms of hyperbox-based models in the hyperbox-brain library can grow and accommodate new classes of data without retraining the whole classifier. Incremental learning algorithms themselves can generate new hyperboxes to represent clusters of new data with potentially new class labels both in the middle of normal training procedure and in the operating time where training has been finished. This property is a key feature for smart life-long learning systems.
 	</div>
 	<p></p>
 </div>