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+FEAST v1.0 
+A feature selection toolbox for C/C++ and MATLAB/OCTAVE
+
+FEAST provides implementations of common mutual information based filter
+feature selection algorithms, and an implementation of RELIEF. All
+functions expect discrete inputs (except RELIEF, which does not depend
+on the MIToolbox), and they return the selected feature indices. These
+implementations were developed to help our research into the similarities
+between these algorithms, and our results are presented in the following paper:
+
+ Conditional Likelihood Maximisation: A Unifying Framework for Mutual Information Feature Selection
+ G.Brown, A.Pocock, M.Lujan, M.-J.Zhao
+ Journal of Machine Learning Research (in press, to appear 2012)
+
+All FEAST code is licensed under the BSD 3-Clause License.
+If you use these implementations for academic research please cite the paper above.
+
+Contains implementations of:
+   mim, mrmr, mifs, cmim, jmi, disr, cife, icap, condred, cmi, relief, fcbf, betagamma
+
+References for these algorithms are provided in the accompanying feast.bib file (in BibTeX format).
+
+MATLAB Example (using "data" as our feature matrix, and "labels" as the class label vector):
+
+>> size(data)
+ans = 
+     (569,30)                                     %% denoting 569 examples, and 30 features
+
+>> selectedIndices = feast('jmi',5,data,labels) %% selecting the top 5 features using the jmi algorithm
+selectedIndices =
+
+    28
+    21
+     8
+    27
+    23
+
+>> selectedIndices = feast('mrmr',10,data,labels) %% selecting the top 10 features using the mrmr algorithm
+selectedIndices =
+
+    28
+    24
+    22
+     8
+    27
+    21
+    29
+     4
+     7
+    25
+
+>> selectedIndices = feast('mifs',5,data,labels,0.7) %% selecting the top 5 features using the mifs algorithm with beta = 0.7
+selectedIndices =
+
+    28
+    24
+    22
+    20
+    29
+
+The library is written in ANSI C for compatibility with the MATLAB mex compiler,
+except for MIM, FCBF and RELIEF, which are written in MATLAB/OCTAVE script.
+
+If you wish to use MIM in a C program you can use the BetaGamma function with
+Beta = 0, Gamma = 0, as this is equivalent to MIM (but slower than the other implementation).
+MIToolbox is required to compile these algorithms, and these implementations
+supercede the example implementations given in that package (they have more robust behaviour 
+when used with unexpected inputs).
+
+MIToolbox can be found at:
+ http://www.cs.man.ac.uk/~gbrown/mitoolbox/
+and v1.03 is included in the ZIP for the FEAST package.
+
+Compilation instructions:
+ MATLAB/OCTAVE - run CompileFEAST.m,
+ Linux C shared library - use the included makefile
+
+Update History
+08/11/2011 - v1.0 - Public Release to complement the JMLR publication.
+