Evaluation of Classifers using Resampling Methods

Type of thesis: Bachelorarbeit / location: Dresden / Status of thesis: Finished theses

Evaluation of a classifier by means of resampling methods like bootstrapping, cross-validation, jack knife, etc. is well established in statistics. Since these methods are computational intensive, they are not yet broadly used within the machine learning community. Because of its structure, the resampling task could benefit from parallelization. The target of this thesis is to elaborate approaches for parallelizing the resampling task in order make it fast and efficient.

Counterpart

Dr.
Christoph Lehmann

Service and Transfer Center

TU Dresden

Statistics, Machine Learning, Deep Learning

TU
Universität
Max
Leibnitz-Institut
Helmholtz
Hemholtz