Thesis opportunities- Hybrid Loss Functions for Support Vector Regression

In machine learning, support vector machines (SVM) are a well-proven family of algorithms for binary classification. The heart of the algorithm is linear regression with a particular loss function, the so-called margin loss. Support vector regression (SVR) is a closely related algorithm for regression problems that uses a loss function often referred to as epsilon-insensitive loss.

We believe that some problems might benefit from combining these two types of loss functions. Similar ideas have been published before (https://arxiv.org/abs/1106.3397), but have not been thoroughly studied, and is therefore not readily available in software packages.

During this project you would implement such an algorithm, either within an existing framework (e.g. liblinear, pytorch, scikit-learn) or from scratch, and analyze how it performs compared to existing algorithms.

Depending on your interests, there are also ideas for additional modifications to investigate, and some parallels to neural network design that you could explore. We look forward to discussing details at the interview.

Prerequisites

You should have an interest in algorithms and familiarity with mathematical optimization and/or machine learning, including some related programming experience.

Contact
For more information about the position, contact:

Erik Hedberg, Algorithm Developer, erik.hedberg@sick.se

or

Sarah Lantz, HR Business Partner, +46 739 10 99 37.

We warmly welcome your application — please submit it no later than October 26th.

SICK is a world-leading supplier of sensors and sensor solutions for industrial applications. We’re part of SICK AG — a global leader in sensor technology with 10,000 employees across 50 countries and headquarters in Freiburg, Germany. Together, we build technology that makes industries more efficient, intelligent, and safe.

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