Statistical support vector machines with optimizations
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Jinran Wu Thesis
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Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. |
Description
This thesis combines support vector machines with statistical models for analyzing data generated by complex processes. The key contribution of the thesis is to propose five regression frameworks aiming for hyperparameter estimation, support vector selection, data modelling with unequal variances, temporal patterns, and cost benefit analysis. A new optimizer is also proposed for high-dimensional optimization.
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| ID Code: | 234509 |
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| Item Type: | QUT Thesis (PhD) |
| Supervisor: | Wang, You-Gan, Burrage, Kevin, & Tian, Glen |
| Keywords: | Approximate loss function, Astmmetric loss, Automatic selection, Forecast, Iterative learning, Loss functions, Parameter Estimation, Probability regularziation, Statistical modeling, Working likelihood |
| DOI: | 10.5204/thesis.eprints.234509 |
| Divisions: | Current > QUT Faculties and Divisions > Faculty of Science Current > Schools > School of Mathematical Sciences |
| Institution: | Queensland University of Technology |
| Deposited On: | 02 Sep 2022 03:59 |
| Last Modified: | 02 Sep 2022 03:59 |
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