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Fine Motor, Social, and Adaptive Function in Autism Spectrum Disorder

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thesis
posted on 2019-09-19, 00:00 authored by Joel Crucitti
This study researched kernel-based methods and max-margin learning for largescale datasets. It advanced several theoretical and practical aspects of kernel-based and max-margin methods at the intersection with Bayesian modelling. New learning methods were proposed to avoid the curse of kernelisation while simultaneously yielding superior accuracy compared with state-of-the-art baselines.

History

Pagination

302 p.

Open access

Yes

Material type

thesis

Resource type

thesis

Language

eng

Degree type

Research doctorate

Degree name

Ph.D.

Copyright notice

The author

Editor/Contributor(s)

M Stokes

Faculty

Faculty of Health

School

School of Psychology