Update README.md

README.md

@@ -20,6 +20,8 @@ Here, we include several implementations for different platforms, in order to ea
 2. **CUDA implementation**: we also provide a GPU implementation (using CUDA) with the aim of speeding up the previous version through GPU's thread-parallelism. 
 3. **Apache Spark**: a Apache Spark's implementation of the algorithm is also included for large-scale problems, which require a distributed processing in order to obtain efficient solutions.
 
+Please, for further information refer to our wiki: https://github.com/sramirez/fast-mRMR/wiki
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 ## Project structure
 
 The code is organized as follows:
@@ -30,6 +32,8 @@ The code is organized as follows:
 * _utils_: this folder contains a data reader program that transforms data in CSV format to the format required by fast-mRMR algorithm (in binary and columnar-wise format) ([+info](https://github.com/sramirez/fast-mRMR/wiki/Data-Reader)). It also includes a data generator method in case we want to generate synthetic data specifying the structure of this data.
 * _examples_: a folder with examples for all versions implemented.   
 
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 ## License
 
 Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements. See the NOTICE file distributed with this work for additional information regarding copyright ownership. The ASF licenses this file to You under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License.  You may obtain a copy of the License at
@@ -38,7 +42,6 @@ http://www.apache.org/licenses/LICENSE-2.0
 
 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
 
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 ## References
 
 [1] _"Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy,"_ Hanchuan Peng, Fuhui Long, and Chris Ding IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 27, No. 8, pp.1226-1238, 2005.