From 91e2b5c8c01974771a04c17cfeb492e8900e69fd Mon Sep 17 00:00:00 2001
From: Parijat Mazumdar <mazumdarparijat@gmail.com>
Date: Tue, 11 Mar 2014 12:25:39 +0530
Subject: [PATCH] algo added in brief

---
 .../multiclass/tree/ID3ClassifierTree.h       | 27 +++++++++++++++++--
 1 file changed, 25 insertions(+), 2 deletions(-)

diff --git a/src/shogun/multiclass/tree/ID3ClassifierTree.h b/src/shogun/multiclass/tree/ID3ClassifierTree.h
index 2d952222e33..e759d2f64bc 100644
--- a/src/shogun/multiclass/tree/ID3ClassifierTree.h
+++ b/src/shogun/multiclass/tree/ID3ClassifierTree.h
@@ -40,8 +40,31 @@
 namespace shogun
 {
 
-/** @brief class ID3ClassifierTree, implements classifier tree for discrete 
- * feature values using the ID3 algorithm.
+/** @brief class ID3ClassifierTree, implements classifier tree for discrete feature 
+ * values using the ID3 algorithm. The training algorithm implemented is as follows :
+ *
+ * function ID3 (R: a set of non-categorical attributes,
+ *		 C: the categorical attribute,
+ *		 S: a training set) returns a decision tree;
+ * begin
+ *	If S consists of records all with the same value for 
+ *	   the categorical attribute, 
+ *	   return a single node with that value;
+ *	If R is empty, then return a single node with as value
+ *	   the most frequent of the values of the categorical attribute
+ *	   that are found in records of S; [note that then there
+ *	   will be errors, that is, records that will be improperly
+ *	   classified];
+ *	Let D be the attribute with largest Gain(D,S) 
+ *	   among attributes in R;
+ *	Let \f${d_j| j=1,2, .., m}\f$ be the values of attribute D;
+ *	Let \f${S_j| j=1,2, .., m}\f$ be the subsets of S consisting 
+ *	   respectively of records with value dj for attribute D;
+ *	Return a tree with root labeled D and arcs labeled 
+ *	  \f$d_1, d_2, .., d_m\f$ going respectively to the trees 
+ *	ID3(R-{D}, C, \f$S_1\f$), .., ID3(R-{D}, C, \f$S_m\f$);
+ * end ID3;
+ *
  */
 class CID3ClassifierTree : public CTreeMachine<id3TreeNodeData>
 {