Learning rates of $l^q$ coefficient regularization learning with Gaussian kernel

19 Dec 2013 Shaobo Lin Jinshan Zeng Jian Fang Zongben Xu

Regularization is a well recognized powerful strategy to improve the performance of a learning machine and $l^q$ regularization schemes with $0<q<\infty$ are central in use. It is known that different $q$ leads to different properties of the deduced estimators, say, $l^2$ regularization leads to smooth estimators while $l^1$ regularization leads to sparse estimators... (read more)

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