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 (https://arxiv.org/abs/1106.3397), 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
There are possibilities to shape this project according to your interests, some options could be:
Prerequisites
We offer:
Contact
For more information about the position, contact:
Erik Hedberg, Algorithm developer, erik.hedberg@sick.se
or
Charlotte Axelsson, HR Manager, +46 739 20 99 50.
Welcome with your application 15th of October at the latest!
Ad has been closed and it is no longer possible to apply.
We are the People and Culture team at SICK Linköping. We can answer all your question on recruitment, life at SICK Linköping, student opportunities or and much more.
Charlotte Axelsson
charlotte.axelsson@sick.se
+46 739 20 99 50
Sarah Lantz
sarah.lantz@sick.se
+46 739 10 99 37
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