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A Classification Data Set for PLM. Information Theory of Learning Sep. 15, 2005. Introduction to Data (1). Handwritten digits (0 ~ 9) From 32x32 bitmaps, non-overlapping 4x4 blocks are extracted. Introduction to Data (2). # of on pixels are counted in each block. (Range: 0 ~ 16)

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A Classification Data Set for PLM

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A Classification Data Set for PLM

Information Theory of Learning

Sep. 15, 2005

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

Introduction to Data (1)

• Handwritten digits (0 ~ 9)

• From 32x32 bitmaps, non-overlapping 4x4 blocks are extracted.

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

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.

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

Introduction to Data (3)

• Data

• 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

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

(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

174 0 0 0 1 1 2 0 0 0 | a = 0

0 178 1 0 1 0 2 0 0 0 | b = 1

0 9 167 0 0 0 0 1 0 0 | c = 2

1 2 0 174 0 1 0 1 2 2 | d = 3

0 11 0 0 168 0 0 0 0 2 | e = 4

0 2 0 1 1 172 1 0 0 5 | f = 5

2 1 0 0 0 1 176 0 1 0 | g = 6

0 0 1 0 1 0 0 174 1 2 | h = 7

1 16 4 7 1 6 2 1 132 4 | i = 8

2 2 0 10 0 4 0 1 3 158 | j = 9

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