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Deep Randomized Learning for Industrial Artificial Intelligence

thesis
posted on 2022-12-09, 01:31 authored by Matthew FelicettiMatthew Felicetti

This dissertation is submitted in total fulfilment of the requirements for the degree of Doctor of Philosophy to the Department of Computer Science and Information Technology, School of Computing, Engineering and Mathematical Sciences, La Trobe University, Victoria, Australia. 

History

School

  • School of Computing, Engineering and Mathematical Sciences

Center or Department

Department of Computer Science and Information Technology

Thesis type

  • Ph. D.

Awarding institution

La Trobe University

Year Awarded

2022

Rights Statement

This thesis contains third party copyright material which has been reproduced here with permission. Any further use requires permission of the copyright owner. The thesis author retains all proprietary rights (such as copyright and patent rights) over all other content of this thesis, and has granted La Trobe University permission to reproduce and communicate this version of the thesis. The author has declared that any third party copyright material contained within the thesis made available here is reproduced and communicated with permission. If you believe that any material has been made available without permission of the copyright owner please contact us with the details.

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    thesisAnalysis of Algorithms and ComplexitySimulation and Modelling

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