[scikit-learn] custom loss function
tevang3 at gmail.com
Wed Sep 13 18:20:32 EDT 2017
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
On 13 September 2017 at 23:51, federico vaggi <vaggi.federico at gmail.com>
> 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 <tevang3 at 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" <t3kcit at 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:
>>> 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 at python.org
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Dr Thomas Evangelidis
CEITEC - Central European Institute of Technology
62500 Brno, Czech Republic
email: tevang at pharm.uoa.gr
tevang3 at gmail.com
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