slide1
Download
Skip this Video
Download Presentation
Artificial Neural Nets

Loading in 2 Seconds...

play fullscreen
1 / 8

Artificial Neural Nets - PowerPoint PPT Presentation


  • 72 Views
  • Uploaded on

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

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about ' Artificial Neural Nets' - sen


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
slide1

Artificial Neural Nets

  • Outline
  • ANN
  • Examples
  • Support Vector Machines
slide2

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.
slide3

Estimation

Minimize

slide4

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])

slide5

SVM

  • Suport vector machines can be generalized to
  • Nonlinear separation.
  • 2. It is an example of linear optimization.
  • The algorithm is a simplex minimization
slide8

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])

ad