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Covergence and adaptation in online kernal methods

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).
Date January 2007
CreatorsPhonphitakchai, Supawan
PublisherUniversity of Sheffield
Source SetsEthos UK
Detected LanguageEnglish
TypeElectronic Thesis or Dissertation

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