Kernels: Regularization and Optimization
Ong, Cheng Soon
This thesis extends the paradigm of machine learning with kernels. This paradigm is based on the idea of generalizing an inner product between vectors to a similarity measure between objects. The kernel implicitly defines a feature mapping between the space of objects and the space of functions, called the reproducing kernel Hilbert space. There have been many successful applications of positive semidefinite kernels in diverse fields. Among the reasons for its success are a theoretically...[Show more]
|01front.pdf||front pages||136.05 kB||Adobe PDF|
|02whole.pdf||whole thesis||2.77 MB||Adobe PDF|
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