Understanding MLP for numeric prediction
by Bashyam, Navaneeth (CSI)
I want to understand how MLP can be used for numeric prediction. I
started with a known function z=x+y; where x and y take values between
0.0 and 1.0 .
I create 5000 data points of different xi's, yi's and zi's.
I set up a MLP with 1 node forming the hidden layer. The simple MLP
looks like >-0-
Weka default MLP gives the following output.
Weights for x : -0.893661993055382
Weights for y : -0.894017186952782
Weights : -4.91766646527907
How do I construct prediction model from the above information.
I tried the following. let us say training set is xt, yt and zp is
predicted value of z.
TANH((xt*(-0.893661993055382)+ yt *(-0.894017186952782)+
The zp's do not match good with z= xt + yt. I am sure I am miising
something here. Because WEKA reports a very high correlation coefficient
for the above MLP run. I would appreciate if someone explains the
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