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Artificial Neural Nets

Artificial Neural Nets. Outline ANN Examples Support Vector Machines. INPUTS:. X. X. X. 1. 2. p. Z 1. Z 2. Z M. HIDDEN. LAYERS. OUTPUTS:. Y. Y. Y. 1. 2. K. 8 tissue samples divided into two groups. G1 = lung tissue with cancerous cells

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Artificial Neural Nets

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  1. Artificial Neural Nets • Outline • ANN • Examples • Support Vector Machines

  2. INPUTS: X X X 1 2 p Z1 Z2 ZM HIDDEN LAYERS OUTPUTS: Y Y Y 1 2 K • 8 tissue samples divided into two groups. • G1 = lung tissue with cancerous cells • G2 = lung tissue with cancerous cells • Obtain the gene expression levels for 200 genes. These are the inputs to the ANN. • Outputs are the tissue group.

  3. Estimation Minimize

  4. How to Use it **************** ANN ******************** library(nnet) pex= read.table("project2/pex23.txt") p = pex[sample(2993,200),] predict(nnet(p[,1:10],p[,24],size=10,subset=rep(c(T,F),c(100,100))))-> y table(round(y),p[,24])

  5. SVM • Suport vector machines can be generalized to • Nonlinear separation. • 2. It is an example of linear optimization. • The algorithm is a simplex minimization

  6. wx+b=0

  7. How to Use it ********************** SVM ********************* library(e1071) svm(p[,1:10],p[,24]) predict(svm(p[,1:10],p[,24])) predict(svm(p[,1:10],factor(p[,24]))) predict(svm(p[1:100,1:10],factor(p[1:100,24])),p[101:200,]) predict(svm(p[1:100,1:10],factor(p[1:100,24])),p[101:200,1:10]) table(predict(svm(p[1:100,1:10],factor(p[1:100,24])),p[101:200,1:10]),p[101:200,24])

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