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## PowerPoint Slideshow about ' Artificial Neural Nets' - sen

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Presentation Transcript

- Outline
- ANN
- Examples
- Support Vector Machines

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.

Minimize

**************** 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])

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

wx+b=0

********************** 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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