Graduate Theses & Dissertations

Support Vector Machines for Automated Galaxy Classification
Support Vector Machines (SVMs) are a deterministic, supervised machine learning algorithm that have been successfully applied to many areas of research. They are heavily grounded in mathematical theory and are effective at processing high-dimensional data. This thesis models a variety of galaxy classification tasks using SVMs and data from the Galaxy Zoo 2 project. SVM parameters were tuned in parallel using resources from Compute Canada, and a total of four experiments were completed to determine if invariance training and ensembles can be utilized to improve classification performance. It was found that SVMs performed well at many of the galaxy classification tasks examined, and the additional techniques explored did not provide a considerable improvement. Author Keywords: Compute Canada, Kernel, SDSS, SHARCNET, Support Vector Machine, SVM

Search Our Digital Collections

Query

Enabled Filters

  • (-) ≠ History
  • (-) ≠ Art history
  • (-) ≠ Martin
  • (-) ≠ Bell
  • (-) ≠ Jamieson
  • (-) = Applied Modeling and Quantitative Methods
  • (-) = Chambers, Cameron Darrin

Filter Results

Date

2014 - 2024
(decades)
Specify date range: Show
Format: 2024/03/28

Name (Any)

Degree

Subject (Topic)