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:
For more information about the position, contact:
Erik Hedberg, Algorithm developer, firstname.lastname@example.org
Charlotte Axelsson, HR Manager, +46 739 20 99 50.
Welcome with your application 15th of October at the latest!