Abstract
Data detailed on have been presented. According to news reporting originating from Hong Kong, People's Republic of China, by NewsRx correspondents, research stated, "We propose the total variation penalized sparse additive support vector machine (TVSAM) for performing classification in the high-dimensional settings, using a mixed $l_{1}$-type functional regularization scheme to induce sparsity and smoothness simultaneously." Nsfc; Cityu Shenzhen Research Institute; Nsf of Jiangxi Province; Hong Kong Rgc; Cityu. The news reporters obtained a quote from the research from City University of Hong Kong: "We establish a representer theorem for TVSAM, which turns the infinite-dimensional problem into a finitedimensional one, thereby providing computational feasibility. Even for the least squares loss, our result fills a gap in the literature when compared with the existing representer theorem. Theoretically, we derive some risk bounds for TVSAM under both exact sparsity and near sparsity, and with arbitrarily specified internal knots."
NETL
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Oxford Univ Press