Computer Science 273B: Kernel-Based Learning

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Description

This course introduces the students to one of the most influential developments in modern machine learning, namely kernel methods. The course will be focused on familiarizing the student with a number of practical kernel-based algorithms (such as “support vector machines”, “kernel Fisher Discrimination”, “kernel principal components analysis” and “Gaussian processes”) and a number of techniques to construct kernels (such as ANOVA kernels, string kernels, graph kernels, diffusion kernels, set kernels). The necessary learning-theoretic preliminaries will be treated as well but it will not be the focus of this course. Applications to real-world problems will serve as examples.

Language: Englis…

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This course introduces the students to one of the most influential developments in modern machine learning, namely kernel methods. The course will be focused on familiarizing the student with a number of practical kernel-based algorithms (such as “support vector machines”, “kernel Fisher Discrimination”, “kernel principal components analysis” and “Gaussian processes”) and a number of techniques to construct kernels (such as ANOVA kernels, string kernels, graph kernels, diffusion kernels, set kernels). The necessary learning-theoretic preliminaries will be treated as well but it will not be the focus of this course. Applications to real-world problems will serve as examples.

Language: English

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There are no frequently asked questions yet. Send an Email to info@springest.com