On 09/13/2017 05:31 PM, Thomas Evangelidis wrote:
What about the SVM? I use an SVR at the end to combine multiple
MLPRegressor predictions using the rbf kernel (linear kernel is not
good for this problem). Can I also implement an SVR with rbf kernel in
Tensorflow using my own loss function? So far I found an example of an
SVC with linear kernel in Tensorflow and nothing in Keras. My
alternative option would be to train multiple SVRs and find through
cross validation the one that minimizes my custom loss function, but
as I said in a previous message, that would be a suboptimal solution
because in scikit-learn the SVR minimizes the default loss function.
Depends on what algorithm you want to use. As Frederico said, SVMs are
usually solved as convex optimization problem on an infinite dimensional
kernel space.
There is no straight-forward way to extend this to arbitrary losses afaik.
You can always make the kernel transformation explicit with Nystroem and
solve a linear regression problem with custom loss on that.
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