Learning with Invariance via Linear Functionals on Reproducing Kernel Hilbert Space
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Learning with Invariance via Linear Functionals on Reproducing Kernel Hilbert Space
Incorporating invariance information is important for many learning problems. To exploit invariances, most existing methods resort to approximations that either lead to expensive optimization problems such as semi-definite programming, or rely on separation oracles to retain tractability. Some methods further limit the space of functions and ...
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Lecture 14: Basic Hilbert Space Theory | Introduction to Functional Analysis | Mathematics | MIT OpenCourseWare
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