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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.
-