In machine learning, support vector machines (SVM) are a well-proven family of algorithms for predicting binary class labels. The heart of the algorithm is linear regression with a particular loss function, the so-called margin loss. A variant called support vector regression (SVR) is used to predict scalar values instead and has a loss function sometimes referred to as epsilon-insensitive loss.

We believe that some problems in machine learning might benefit from combining these two types of loss functions. Similar ideas have been published before (, but have received little attention so far, meaning this type of loss function has not been thoroughly studied, and is not readily available in open-source software packages.

The master thesis work focuses on the following

  • Implement an SVM/SVR type algorithm with this hybrid loss function,
    • either in an existing framework (e.g. PyTorch, scikit-learn or OpenCV),
    • or from scratch (e.g. in Python or C++).
  • Analyze the performance of this hybrid approach compared to conventional SVM or SVR, in terms of
    • prediction accuracy, as well as
    • convergence speed and robustness.
  • Try to characterize for what kind of problems, if any, this approach gives benefits.

There are possibilities to shape this project according to your interests, some options could be:

  • Contribute the implementation back to an open-source framework.
  • Summarize your findings in a research article.
  • Survey the literature for similar algorithms, and focus on a broader analysis.


  • An interest in understanding algorithms.
  • Familiarity with mathematical optimization or machine learning.
  • Some programming experience, not necessarily in the languages above.

We offer:

  • A welcoming company culture.
  • Supervisor with many years of experience in thesis supervision.


For more information about the position, contact:

Erik Hedberg, Algorithm developer,


Charlotte Axelsson, HR Manager, +46 739 20 99 50.

Welcome with your application 15th of October at the latest!