Commit Inicial

data/tanveer/letter/letter-recognition.names

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+1. Title: Letter Image Recognition Data 
+
+2. Source Information
+   -- Creator: David J. Slate
+     -- Odesta Corporation; 1890 Maple Ave; Suite 115; Evanston, IL 60201
+   -- Donor: David J. Slate (dave@math.nwu.edu) (708) 491-3867   
+   -- Date: January, 1991
+
+3. Past Usage:
+   -- P. W. Frey and D. J. Slate (Machine Learning Vol 6 #2 March 91):
+	"Letter Recognition Using Holland-style Adaptive Classifiers".
+
+   	The research for this article investigated the ability of several
+	variations of Holland-style adaptive classifier systems to learn to
+	correctly guess the letter categories associated with vectors of 16
+	integer attributes extracted from raster scan images of the letters.
+	The best accuracy obtained was a little over 80%.  It would be
+	interesting to see how well other methods do with the same data.
+
+4. Relevant Information:
+
+   The objective is to identify each of a large number of black-and-white
+   rectangular pixel displays as one of the 26 capital letters in the English
+   alphabet.  The character images were based on 20 different fonts and each
+   letter within these 20 fonts was randomly distorted to produce a file of
+   20,000 unique stimuli.  Each stimulus was converted into 16 primitive
+   numerical attributes (statistical moments and edge counts) which were then
+   scaled to fit into a range of integer values from 0 through 15.  We
+   typically train on the first 16000 items and then use the resulting model
+   to predict the letter category for the remaining 4000.  See the article
+   cited above for more details.
+
+5. Number of Instances: 20000
+
+6. Number of Attributes: 17 (Letter category and 16 numeric features)
+
+7. Attribute Information:
+	 1.	lettr	capital letter	(26 values from A to Z)
+	 2.	x-box	horizontal position of box	(integer)
+	 3.	y-box	vertical position of box	(integer)
+	 4.	width	width of box			(integer)
+	 5.	high 	height of box			(integer)
+	 6.	onpix	total # on pixels		(integer)
+	 7.	x-bar	mean x of on pixels in box	(integer)
+	 8.	y-bar	mean y of on pixels in box	(integer)
+	 9.	x2bar	mean x variance			(integer)
+	10.	y2bar	mean y variance			(integer)
+	11.	xybar	mean x y correlation		(integer)
+	12.	x2ybr	mean of x * x * y		(integer)
+	13.	xy2br	mean of x * y * y		(integer)
+	14.	x-ege	mean edge count left to right	(integer)
+	15.	xegvy	correlation of x-ege with y	(integer)
+	16.	y-ege	mean edge count bottom to top	(integer)
+	17.	yegvx	correlation of y-ege with x	(integer)
+
+8. Missing Attribute Values: None
+
+9. Class Distribution:
+ 	789 A	   766 B     736 C     805 D	 768 E	   775 F     773 G
+ 	734 H	   755 I     747 J     739 K	 761 L	   792 M     783 N
+ 	753 O	   803 P     783 Q     758 R	 748 S	   796 T     813 U
+ 	764 V	   752 W     787 X     786 Y	 734 Z