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+-*- mode: text -*-
+
++----------------------------------------------------------------------+
+| This archive contains a simple implementation of the Conditional     |
+| Mutual Information Maximization for feature selection.               |
++----------------------------------------------------------------------+
+| Written by François Fleuret                                          |
+| Contact <francois.fleuret@epfl.ch> for comments & bug reports        |
+| Copyright (C) 2004 EPFL                                              |
++----------------------------------------------------------------------+
+
+$Id: README,v 1.3 2007-08-23 08:36:50 fleuret Exp $
+
+0/ INTRODUCTION
+
+  The CMIM feature selection scheme is designed to select a small
+  number of binary features among a very large set in a context of two
+  class classification. It consists in picking features one after
+  another to maximize the conditional mutual information between the
+  selected feature and the class to predict given any one of the
+  features already picked. Such a criterion picks features which are
+  both individually informative yet pairwise weakly dependent. CMIM
+  stands for Conditional Mutual Information Maximization. See
+
+  Fast Binary Feature Selection with Conditional Mutual Information
+  Francois Fleuret
+  JMLR 5 (Nov): 1531--1555, 2004
+  http://www.jmlr.org/papers/volume5/fleuret04a/fleuret04a.pdf
+
+1/ INSTALLATION
+
+  To compile and test, just type 'make test'
+
+  This small test consists of generating a sample set for a toy
+  problem and testing CMIM, MIM and a random feature selection with
+  the naive Bayesian learner.  The two populations of the toy problem
+  live in the [0, 1]^2 square. The positive population is in x^2+y^2 <
+  1/4 and the negative population is everything else.  Look at
+  create_samples.cc for more details.  The features are responses of
+  linear classifiers generated at random.
+
+2/ DATA FILE FORMAT
+
+  Each data file, either for training or testing, starts with the
+  number of samples and the number of features. Then follow for every
+  single sample two lines, one with the value of the features (0/1)
+  and one with the value of the class to predict (0/1).  Check the
+  train.dat and test.dat generated by create_samples to get an
+  example.
+
+  The test file has the same format, and the real class is used to
+  estimate the error rates.  During test, the response of the naive
+  bayse before thresholding is saved in a result file (3rd parametre
+  of the --test option)
+
+3/ OPTIONS
+
+  --silent
+
+    Switch off all the outputs to stdout
+
+  --feature-selection <random|mim|cmim>
+
+    Selects the feature selection method
+
+  --classifier <bayesian|perceptron>
+
+    Selects the classifier type
+
+  --error <standard|ber>
+
+    Choses which error to minimize during bias estimation for the CMIM
+    + naive Bayesian.
+
+    standard = P(f(X) = 0, Y = 1) + P(f(X) = 1, Y = 0)
+
+    ber      = (P(f(X) = 0 | Y = 1) + P(f(X) = 1 | Y = 0))/2
+
+  --nb-features <int: nb of features>
+
+    Selects the number of selected features
+
+  --cross-validation <file: data set> <int: nb test samples> <int: nb loops>
+
+    Do cross-validation
+
+  --train <file: data set> <file: classifier>
+
+    Build a classifier and save it on disk
+
+  --test <file: classifier> <file: data set> <file: result>
+
+    Load a classifier and test it on a dataset
+
+4/ LICENCE
+
+  This program is free software; you can redistribute it and/or modify
+  it under the terms of the GNU General Public License version 3 as
+  published by the Free Software Foundation.
+
+  This program is distributed in the hope that it will be useful, but
+  WITHOUT ANY WARRANTY; without even the implied warranty of
+  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
+  General Public License for more details.