diff --git a/README b/README
deleted file mode 100644
index 32d1cb1..0000000
--- a/README
+++ /dev/null
@@ -1,72 +0,0 @@
-Naive Bayes Classifier implemented in Java.
-==================
-
-Nothing special. It works and is well documented, so you should get it running without wasting too much time searching for other alternatives on the net.
-
-Here is an excerpt from the example. The classifier will classify strings as either positive or negative sentiment. Please refer to the full example for a more detailed documentation.
-
-```java
-Classifier<String, String> bayes = new BayesClassifier<String, String>();
-
-// Two examples to learn from.
-String[] positiveText = "I love sunny days".split("\\s");
-String[] negativeText = "I hate rain".split("\\s");
-
-// Learn by classifying examples. New categories can be added on the fly,
-// when they are first used.
-bayes.learn("positive", Arrays.asList(positiveText));
-bayes.learn("negative", Arrays.asList(negativeText));
-
-// Here are two unknown sentences to classify.
-String[] unknownText1 = "today is a sunny day".split("\\s");
-String[] unknownText2 = "there will be rain".split("\\s");
-
-System.out.println( // will output "positive"
-    bayes.classify(Arrays.asList(unknownText1)).getCategory());
-System.out.println( // will output "negative"
-    bayes.classify(Arrays.asList(unknownText2)).getCategory());
-
-// Get more detailed classification result.
-((BayesClassifier<String, String>) bayes).classifyDetailed(
-    Arrays.asList(unknownText1));
-
-// Change the memory capacity.
-bayes.setMemoryCapacity(500);
-```
-
-Forgetful learning
-------------------
-
-This classifier is forgetful. This means, that the classifier will forget recent classifications it uses for future classifications after - defaulting to 200 - classifications learned.
-This will ensure, that the classifier can react to ongoing changes in the user's habbits.
-
-Possible Performance issues
-------------------
-
-Performance improvements, I am currently thinking of:
-
-- Store the natural logarithms of the feature probabilities and add them together instead of multiplying the probability numbers
-
-The MIT License (MIT)
-------------------
-
-Copyright (c) 2012 Philipp Nolte
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in
-all copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
-THE SOFTWARE.
-
diff --git a/README.md b/README.md
index 5c458a9..790e31f 100644
--- a/README.md
+++ b/README.md
@@ -1,19 +1,25 @@
-Naive Bayes Classifier implemented in Java.
+Java Naive Bayes Classifier
 ==================
 
 Nothing special. It works and is well documented, so you should get it running without wasting too much time searching for other alternatives on the net.
 
-Here is an excerpt from the example. The classifier will classify strings as either positive or negative sentiment. Please refer to the full example for a more detailed documentation.
+Example
+------------------
+
+Here is an excerpt from the example. The classifier will classify sentences (arrays of features) as sentences with either positive or negative sentiment. Please refer to the full example for a more detailed documentation.
 
 ```java
+// Create a new bayes classifier with string categories and string features.
 Classifier<String, String> bayes = new BayesClassifier<String, String>();
 
 // Two examples to learn from.
 String[] positiveText = "I love sunny days".split("\\s");
 String[] negativeText = "I hate rain".split("\\s");
 
-// Learn by classifying examples. New categories can be added on the fly,
-// when they are first used.
+// Learn by classifying examples.
+// New categories can be added on the fly, when they are first used.
+// A classification consists of a category and a list of features
+// that resulted in the classification in that category.
 bayes.learn("positive", Arrays.asList(positiveText));
 bayes.learn("negative", Arrays.asList(negativeText));
 
@@ -30,7 +36,9 @@ System.out.println( // will output "negative"
 ((BayesClassifier<String, String>) bayes).classifyDetailed(
     Arrays.asList(unknownText1));
 
-// Change the memory capacity.
+// Change the memory capacity. New learned classifications (using
+// learn method are stored in a queue with the size given here and
+// used to classify unknown sentences.
 bayes.setMemoryCapacity(500);
 ```