1. A Classification Data Set for PLM
Information Theory of Learning
Sep. 15, 2005
(c) SNU CSE Biointelligence Lab,

2. Introduction to Data (1)
Handwritten digits (0 ~ 9)
From 32x32 bitmaps, non-overlapping 4x4 blocks are extracted.
(c) SNU CSE Biointelligence Lab,

3. Introduction to Data (2)
# of on pixels are counted in each block. (Range: 0 ~ 16)
If # >   1, otherwise 0
Original 32x32 bitmap is reduced to 8x8 binary matrix. 1
(c) SNU CSE Biointelligence Lab,

4. Introduction to Data (3)
train.txt: 3823 examples
test.txt: 1797 examples
Representation
In the text files, each row consists of 64 binary values with its label attached at 65-th column.
Class distribution 1 2 3 4 5 6 7 8 9
Train
376
389
380
387
377
382
Test
178
182
177
183
181
179
174
180
(c) SNU CSE Biointelligence Lab,

5. (c) 2000-2005 SNU CSE Biointelligence Lab, http://bi.snu.ac.kr

6. (c) 2000-2005 SNU CSE Biointelligence Lab, http://bi.snu.ac.kr
Preliminary Result
k-nn result (k = 3) on the test set
Accuray: 93.10% (ratio of correctly classified)
a b c d e f g h i j <-- classified as
| a = 0
| b = 1
| c = 2
| d = 3
| e = 4
| f = 5
| g = 6
| h = 7
| i = 8
| j = 9
(c) SNU CSE Biointelligence Lab,