Data Mining - Classification by Backpropagation, undefined for Data Mining. Moradabad Institute of Technology (MIT)

Data Mining

Description: This document about Classification by Backpropagation, Neural Network as a Classifier, A Neuron , A Multi-Layer Feed-Forward Neural Network , How A Multi-Layer Neural Network Works?, Initial input, weight, and bias values .
Showing pages  1  -  4  of  12


 !
"#
Backpropagation:$neural network 
%!!
&
$  '# $  &  ( 
'weight'
     network learns by
adjusting the weights        
&
$ &   connectionist learning   
'


'#
 
Weakness
)
*&!!
!++'#!,,+-
.!/!
'&,,-
'#
Strength
0!
$!&!
12&2
%&'!&2'
$!
!&3&
&'#

 4
$567
n2x!!
&&
k
-
f
weighted
sum
Input
vector x
output y
Activation
function
weight
vector w
w0
w1
wn
x0
x1
xn
)sign(y
ExampleFor
n
0i
kii xw


$2)!828''#
Output layer
Input layer
Hidden layer
Output vector
Input vector: X
wij
i
jiijj OwI
j
I
je
O
1
1
))(1( jjjjj OTOOErr
jk
k
kjjj wErrOOErr
)1(
ijijij OErrlww )(
jjj Errl)(
The preview of this document ends here! Please or to read the full document or to download it.
Document information
Embed this document:
Docsity is not optimized for the browser you're using. In order to have a better experience please switch to Google Chrome, Firefox, Internet Explorer 9+ or Safari! Download Google Chrome