Naive bayes classifier

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  • 1. Naive Bayes classifier, 200914359TH, MAY, 2013GLOBAL SCHOOL OF MEDIA, SOONGSIL UNIVERSITYMEDIA MATHEMATICS FOR B.S COURSE

2. 3. 0 9 , .Ex) 2(10) 10(2) , .2 2 2 4. 5. 6. .) Helvetica Helvetica0 9 (10 )20 1 2 3 4 5 6 7 8 9 7. , , , , , , - 8. 1. > , 2. > 1 , 3. > ? ?4. 9. , ? THE MNIST DATABASE of handwritten digits 6 0 9 .http://yann.lecun.com/exdb/mnist/ 28x28 , 10. Naive Bayes classifier . ? , 0 9 -> 0.1 ? -> ( ) ?1) 0 2) 1 11. Navie Bayes classifier - likelihood0,2 on = 0,2 on / = 2 / 3 = 0.6666 12. Naive Bayes classifier 3 ,{F0,0 = 0, F0,1 = 0, F0,2 = 1, ,F8,8=0} = x 0,0 8,8 0.9 13. Naive Bayes classifier1 0.12 0.13 0.14 0.15 0.16 0.17 0.18 0.19 0.10 0.11 0.012 0.053 0.054 0.305 0.806 0.907 0.058 0.609 0.500 0.801 0.052 0.013 0.904 0.805 0.906 0.907 0.058 0.609 0.500 0.80 3,1 5,5 14. Naive Bayes classifier2 3 = 0.1 = 0.12,2 On0.82,2 On0.95,4 On0.15,4 On0.86,4 Off0.16,4 Off0.80.0008 0.0567< 15. Naive Bayes classifier 2 3 = 0.1 = 0.12,2 On0.82,2 On0.95,4 On0.15,4 On0.86,4 Off0.16,4 Off0.80.00008 0>8,8 On0.018,8 On0 16. , 0 . 0 , , log10 . , . 17. Pseudo codeInt bayesclassifier(input[][], likelihood[][][]){Int result[10];For(I = 0; I < 10; i++){Result[i] = 0.1;For(j = 0; j < 28; j++)For(k=0; k

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