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🔗 Source: studentsrepo.um.edu.my
📊 Size: 16.22 MB
📄 Pages: 252 pages
⬇️ Downloads: 65

Summary

Mohd Adli Bin Md Ali's PhD thesis explores the development and usage of Self-Organizing Maps (SOM) in high energy physics analysis with high performance computing. The research investigates the effects of SOM hyperparameters on clustering outcomes and demonstrates the use of Kullback-Leibler divergence and Multivariate Bhattacharyya Distance as validation parameters. It also shows that a classification model can be created by combining SOM with Linear Discrimination Analysis and evaluates the performance of this model. Additionally, the thesis demonstrates unsupervised clustering of particle physics datasets using SOM and SOM+Dirichelet Gaussian Mixture Modelling.

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Description

Mohd Adli Bin Md Ali's PhD thesis explores the development and usage of Self-Organizing Maps (SOM) in high energy physics analysis with high performance...

Technical Information

  • File Format: PDF
  • File Size: 16.22 MB
  • Pages: 252
  • Language: EN
  • Total Downloads: 65
  • Last Updated: 2 weeks ago

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