<div dir="ltr"><div class="gmail_default" style="font-size:large;color:rgb(0,0,0)">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.<br></div></div><div class="gmail_extra"><br><div class="gmail_quote">On 13 September 2017 at 23:51, federico vaggi <span dir="ltr"><<a href="mailto:vaggi.federico@gmail.com" target="_blank">vaggi.federico@gmail.com</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr">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).</div><div class="HOEnZb"><div class="h5"><br><div class="gmail_quote"><div dir="ltr">On Wed, 13 Sep 2017 at 14:31 Thomas Evangelidis <<a href="mailto:tevang3@gmail.com" target="_blank">tevang3@gmail.com</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="auto">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. </div><div class="gmail_extra"><br><div class="gmail_quote">Dne 13. 9. 2017 20:48 napsal uživatel "Andreas Mueller" <<a href="mailto:t3kcit@gmail.com" target="_blank">t3kcit@gmail.com</a>>:</div></div><div class="gmail_extra"><div class="gmail_quote"><br type="attribution"><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
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<div class="m_-6115041905439139857m_-8519445550891590791m_3816762298944024593moz-cite-prefix">On 09/13/2017 01:18 PM, Thomas
Evangelidis wrote:<br>
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<div class="gmail_default" style="color:rgb(0,0,0)"><font size="4"></font></div>
<div class="gmail_default" style="color:rgb(0,0,0)"><font size="4">Thanks again for the clarifications Sebastian!</font></div>
<div class="gmail_default" style="color:rgb(0,0,0)"><font size="4"><br>
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<div class="gmail_default" style="color:rgb(0,0,0)"><font size="4">Keras has a Scikit-learn API with the KeraRegressor
which implements the Scikit-Learn MLPRegressor interface:</font></div>
<div class="gmail_default" style="color:rgb(0,0,0)"><font size="4"><br>
</font></div>
<div class="gmail_default"><font size="4"><font face="Arial, Helvetica Neue, Helvetica, sans-serif" color="#242729"><a href="https://keras.io/scikit-learn-api/" target="_blank">https://keras.io/scikit-learn-<wbr>api/</a></font><br>
</font></div>
<div class="gmail_default" style="color:rgb(0,0,0)"><span><font size="4"><br>
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<div class="gmail_default" style="color:rgb(0,0,0)"><span><font size="4">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.</font></span></div>
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<font size="4">I think using keras is probably the way to go for
you.</font><br>
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<br></blockquote></div><br><br clear="all"><br>-- <br><div class="gmail_signature" data-smartmail="gmail_signature"><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div>
<p style="margin-bottom:0cm" align="LEFT"><span style="color:rgb(0,0,0)"><span style="font-family:arial,helvetica,sans-serif"><font size="2">======================================================================</font></span></span></p>
<p style="margin-bottom:0cm" align="LEFT"><span style="color:rgb(0,0,0)"><span style="font-family:arial,helvetica,sans-serif"><font size="2">Dr Thomas Evangelidis</font></span></span></p>
<p style="margin-bottom:0cm" align="LEFT"><span style="color:rgb(0,0,0)"><span style="font-family:arial,helvetica,sans-serif"><font size="2">Post-doctoral Researcher<br></font></span></span></p><span style="color:rgb(0,0,0)"><span style="font-family:arial,helvetica,sans-serif"><font size="2">CEITEC - Central European Institute of Technology<br>Masaryk University<br>Kamenice 5/A35/2S049, <br>62500 Brno, Czech Republic <br></font></span></span></div><div dir="ltr"><span style="color:rgb(0,0,0)"><br></span><p style="margin-bottom:0cm" align="LEFT"><span style="color:rgb(0,0,0)"><span style="font-family:arial,helvetica,sans-serif"><font size="2">email: <a href="mailto:tevang@pharm.uoa.gr" target="_blank">tevang@pharm.uoa.gr</a></font></span></span></p>
<p style="margin-bottom:0cm" align="LEFT"><span style="color:rgb(0,0,0)"><span style="font-family:arial,helvetica,sans-serif"><font size="2"> <a href="mailto:tevang3@gmail.com" target="_blank">tevang3@gmail.com</a></font></span></span></p>
<p style="margin-bottom:0cm" align="LEFT"><span style="color:rgb(0,0,0)"><span style="font-family:arial,helvetica,sans-serif"><font size="2"><br>website:
<a href="https://sites.google.com/site/thomasevangelidishomepage/" target="_blank">https://sites.google.com/site/thomasevangelidishomepage/</a></font></span></span></p><span style="color:rgb(0,0,0)"><br></span><p style="margin-bottom:0cm" align="LEFT">
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