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Title: Covergence and adaptation in online kernal methods
Author: Phonphitakchai, Supawan
ISNI:       0000 0001 3489 6894
Awarding Body: University of Sheffield
Current Institution: University of Sheffield
Date of Award: 2007
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Learning System is a method to approximate an underlying function from a finite observation data. Although the batch solution has been widely used to investigate the approximation function, it provides the disadvantage in terms of computational expensive. Online solution increases the importance as it performs a better ability in handling large, realife training data. The problem of investigating the approximation function is posed on reproducing kernel Hilbert spaces (RKHS) as the hypothesis space. RKHS provides a natural framework when some unknown function is estimated using a finite observation data. Solving for the approximation function is achieved by minimising a regularised risk functional where a regularisation parameter is taken into account to prevent the ill-posed condition. The solution of online minimisation is provided based on the iterative method called stochastic gradient descent (SOD).
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available