Body: <p>I'm comparing the results of an Newton optimizer for a modified version of SVM ( a generalized quadratic loss, similar to the one stated in:</p>
  
  <p><a href="http://www.frontiersinai.com/ecai/ecai2004/ecai04/pdf/p0628.pdf" rel="nofollow noreferrer">A generalized quadratic loss for SVM</a></p>
  
  <p>) with classic SVM^light for regression. The problem is that it's able to overfit the data (UCI Yacht data-set) but I can't reach the generalization results of SVM^light. I've tried several hyper-parameters grids. I'm solving the primal problem. I'll send you my code if you need it. Any suggestion?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-11-26T12:56:05.620Z
Comments.Text: I figured out I've not shuffled the whole data-set (training+validation+test).
  Now it generalizes in a better way (at least on the UCI Yacht data-set)
Comments.UserDisplayName: filippo portera
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-12-17T10:33:24.023Z
Comments.Text: I was wrong. I tried a different shuffle and the problem still persist.
Comments.UserDisplayName: Filippo Portera
Tags: <objective-functions><support-vector-machine>
Title: A generalized quadratic loss and Newton iteration for Support Vector Regression, why doesn't it generalize well?
UserDisplayName: filippo portera
ThreadText: # A generalized quadratic loss and Newton iteration for Support Vector Regression, why doesn't it generalize well?
  
  (Asked by: username_0 on 2019-11-26)
  
  I'm comparing the results of an Newton optimizer for a modified version of SVM ( a generalized quadratic loss, similar to the one stated in:
  
  [A generalized quadratic loss for SVM](http://www.frontiersinai.com/ecai/ecai2004/ecai04/pdf/p0628.pdf)
  
  ) with classic SVM^light for regression. The problem is that it's able to overfit the data (UCI Yacht data-set) but I can't reach the generalization results of SVM^light. I've tried several hyper-parameters grids. I'm solving the primal problem. I'll send you my code if you need it. Any suggestion?
  
  **Tags:** objective-functions, support-vector-machine
  
  > **Comments:**
  > **username_0**: I figured out I've not shuffled the whole data-set (training+validation+test). Now it generalizes in a better way (at least on the UCI Yacht data-set)  
  > **username_1**: I was wrong. I tried a different shuffle and the problem still persist.  
  
  ---
  
  ## 0 answers
  
  