The performance of semi-supervised Laplacian regularized regression with the least square loss
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- 作者：Baohuai Sheng
- 所属单位：数理与信息工程学院
- 文献类型：期刊
- 发表时间：2017-01-01
- 发表刊物：International Journal of Wavelets, Multiresolution and Information Processing
- 卷号：Vol.15
- 期号：No.2
- 页面范围：1750016
- Issn号：0219-6913;1793-690X
- 是否译文：否
- 关键字：Semi-supervised LapRLS; convex analysis; Gateaux derivative; Gauss kernel; learning rate
- 摘要：The capacity convergence rate for a kind of kernel regularized semi-supervised Laplacian learning algorithm is bounded with the convex analysis approach. The algorithm is a graph-based regression whose structure shares the feature of both the kernel regul
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