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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

  • Outline

  • ANN

  • Examples

  • Support Vector Machines


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.


Estimation

Minimize


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])


SVM

  • Suport vector machines can be generalized to

  • Nonlinear separation.

  • 2. It is an example of linear optimization.

  • The algorithm is a simplex minimization


wx+b=0


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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