Kernel methods in machine learning
We review machine learning methods employing positive definite kernels. These methods formulate learning and estimation problems in a reproducing kernel Hilbert space (RKHS) of functions defined on the data domain, expanded in terms of a kernel. Working in linear spaces of function has the benefit of facilitating the construction and analysis of learning algorithms while at the same time allowing large classes of functions. The latter include nonlinear functions as well as functions...[Show more]
|Collections||ANU Research Publications|
|Source:||Annals of Statistics|
|Access Rights:||Open Access|
|01_Thomas Hofman_Kernel_methods_in_machine_2007.pdf||Published Version||424.09 kB||Adobe PDF|
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