I said that I want to make a Support Vector Regressor using the rbf kernel to minimize my own loss function. Never mentioned about classification and hinge loss.
On 13 September 2017 at 23:51, federico vaggi <vaggi.feder...@gmail.com> wrote: > 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 >> > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > > -- ====================================================================== Dr Thomas Evangelidis Post-doctoral Researcher CEITEC - Central European Institute of Technology Masaryk University Kamenice 5/A35/2S049, 62500 Brno, Czech Republic email: tev...@pharm.uoa.gr teva...@gmail.com website: https://sites.google.com/site/thomasevangelidishomepage/
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