You are confusing the kernel with the loss function. SVM minimize a well defined hinge loss on a space that's implicitly defined by a kernel mapping (or, in feature space if you use a linear kernel).
On Wed, 13 Sep 2017 at 14:31 Thomas Evangelidis <teva...@gmail.com> 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. > > Dne 13. 9. 2017 20:48 napsal uživatel "Andreas Mueller" <t3k...@gmail.com > >: > > >> >> On 09/13/2017 01:18 PM, Thomas Evangelidis wrote: >> >> >> Thanks again for the clarifications Sebastian! >> >> Keras has a Scikit-learn API with the KeraRegressor which implements the >> Scikit-Learn MLPRegressor interface: >> >> https://keras.io/scikit-learn-api/ >> >> Is it possible to change the loss function in KerasRegressor? I don't >> have time right now to experiment with hyperparameters of new ANN >> architectures. I am in urgent need to reproduce in Keras the results >> obtained with MLPRegressor and the set of hyperparameters that I have >> optimized for my problem and later change the loss function. >> >> I think using keras is probably the way to go for you. >> >> _______________________________________________ >> scikit-learn mailing list >> scikit-learn@python.org >> https://mail.python.org/mailman/listinfo/scikit-learn >> >> _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn >
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