the same result.
Only the contribution of the PC's seems to be different.
I would appreciate any help. Thank you.
--
Yukihiro Ishii <[EMAIL PROTECTED]>
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Dear List,
I am trying to solve a problem by the neural network method(library:
nnet). The problem is to express Weight in terms of Age , Sex and
Height for twenty people(thius is an example given by Tanake in
"Introduction to Neural Networks by NEUROSIM/L"(2003, in Japanese))..
Dear List,
I am trying to solve a problem by the neural network method(library:
nnet). The problem is to express Weight in terms of Age , Sex and
Height for twenty people(thius is an example given by Tanake in
"Introduction to Neural Networks by NEUROSIM/L"(2003, in Japanese))..
Dear List,
I am trying to solve a problem by the neural network method(library:
nnet). The problem is to express Weight in terms of Age , Sex and Height
for twenty people. The data frame consists of 20 observations with four
variables: Sex, Age, Height and Weight. Sex is treated as
Dear List,
I am trying to solve a problem by the neural network method(library:
nnet). The problem is to express Weight in terms of Age , Sex and Height
for twenty people. The data frame consists of 20 observations with four
variables: Sex, Age, Height and Weight. Sex is treated as
Hi,
I am an old hand at chemistry but a complete beginner at statistics
including R computations.
My question is whether you can carry out nonlinear
multivariate regression analysis in R using neural networks, where the
output variable can range from -Inf to + Inf., unlike discr
