Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.604048
Title: Applications of statistical learning theory to signal processing problems
Author: Hill, S.
Awarding Body: University of Cambridge
Current Institution: University of Cambridge
Date of Award: 2003
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Abstract:
The dissertation focuses on the applicability of Support Vector Regression (SVR) in signal processing contexts. This is shown to be particularly well-suited to filtering in alpha-stable noise environments, and a further slight modification is proposed to this end. The main work in this dissertation on SVR is on the application to audio filtering based on perceptual criteria. This appears an ideal solution to the problem due to the fact that the loss function typically used by perceptual audio filtering practitioners incorporates a region of zero loss, as does SVR. SVR is extended to the problem of complex-valued regression, for application in the audio filtering problem to the frequency domain. This is with regions of zero loss that are both square and circular, and the circular case is extended to the problem of vector-valued regression. Three experiments are detailed with a mix of both good and poor results, and further refinements are proposed. Polychotomous, or multi-category classification is then studied. Many previous attempts are reviewed, and compared. A new approach is proposed, based on a geometrical structure. This is shown to overcome many of the problems identified with previous methods in addition to being very flexible and efficient in its implementation. This architecture is also derived, for just the three-class case, using a complex-valued kernel function. The general architecture is used experimentally in three separate implementations to give a demonstration of the overall approach. The methodology is shown to achieve results comparable to those of many other methods, and to include many of them as special cases. Further possible refinements are proposed which should drastically reduce optimisation times for so-called 'all-together' methods.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.604048  DOI: Not available
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