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Properties of regularisation operators in learning theory

Friday 8th February 2008 - 11:00 to 12:00
INI Seminar Room 2

We consider the properties of a large class of learning algorithms defined in terms of classical regularization operators for ill-posed problems. This class includes regularized least-squares, Landweber method, $\nu$-methods and truncated singular value decomposition on hypotyesis spaces of vector-valued functions defined in terms of suitable reproducing kernels. In particular universal consistency, minimax rates and statistical adaptation of the methods we will be discussed.

University of Cambridge Research Councils UK
    Clay Mathematics Institute London Mathematical Society NM Rothschild and Sons