Combining and evaluating regression methods for galaxyredshift estimates

Title

Combining and evaluating regression methods for galaxyredshift estimates

 Author

Boon, K.

Contributor

Loog, M. (mentor)

Faculty

Electrical Engineering, Mathematics and Computer Science

 Department

Intelligent Systems

 Date

2017-03-03

 Abstract

In this thesis I develop a Machine Learning method to combine galactic redshift estimates of previous authors into an aggregate estimate. I investigate weather combining earlier results into a single estimate is a worthwhile effort. Disagreement between the earlier results is used as a metric the quality of estimation. This disagreement is exploited using kernel density estimation to iteratively selecting a subset of the problem that is smaller, but harder to solve. The iteration is repeated until either the problem is solved, or the remaining subset is too difficult to solve reliably.

 To reference this document use:

http://resolver.tudelft.nl/uuid:3c4c5c99-c866-454d-ae15-5fb62eee0ba8

 Part of collection

Student theses

 Document type

master thesis

 Rights

(c) 2017 K. Boon
Files
PDF thesis kees boon.pdf 1.72 MB
Close viewer