My bad, I looked at your question in the context of your 2nd e-mail in this topic where you talked about custom loss functions and SVR.
On Wed, 13 Sep 2017 at 15:20 Thomas Evangelidis <teva...@gmail.com> wrote: > 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/ > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn >
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