Statistical Rates of Convergence for Functional Partially Linear Support Vector Machines for Classification
Statistical Rates of Convergence for Functional Partially Linear Support Vector Machines for Classification
复制标题
用于分类的功能性部分线性支持向量机的统计收敛率
DOI:
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发表时间:
2022
影响因子:
6
通讯作者:
Heng Lian
中科院分区:
文献类型:
--
作者:
Yingying Zhang;Yanyong Zhao;Heng Lian
In this paper, we consider the learning rate of support vector machines with both a functional predictor and a high-dimensional multivariate vectorial predictor. Similar to the literature on learning in reproducing kernel Hilbert spaces, a source condition and a capacity condition are used to characterize the convergence rate of the estimator. It is highly non-trivial to establish the possibly faster rate of the linear part. Using a key basic inequality comparing losses at two carefully constructed points, we establish the learning rate of the linear part which is the same as if the functional part is known. The proof relies on empirical processes and the Rademacher complexity bound in the semi-nonparametric setting as analytic tools, Young’s inequality for operators, as well as a novel “approximate convexity” assumption.
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DOI:
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