• 论文 •

### 基于拟凸损失的核正则化成对学习算法的收敛速度

1. 1. 浙江工商大学统计与数学学院, 杭州 310018;2. 绍兴文理学院应用统计系,  绍兴 312000;3. 华中农业大学信息学院,武汉 430070
• 出版日期:2020-03-25 发布日期:2020-05-30

WANG Shuhua, WANG Yingjie, CHEN Zhenlong, SHENG Baohuai. The Convergence Rate for Kernel-Based Regularized Pair Learning Algorithm with a Quasiconvex Loss[J]. Journal of Systems Science and Mathematical Sciences, 2020, 40(3): 389-409.

### The Convergence Rate for Kernel-Based Regularized Pair Learning Algorithm with a Quasiconvex Loss

WANG Shuhua 1,2 ,WANG Yingjie3 ,CHEN Zhenlong1 ,SHENG Baohuai2

1. 1. School of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou 310018; 2. Department of Applied Statistics, Shaoxing University, Shaoxing 312000; 3. College of Informatics, Huazhong Agricutural University, Wuhan 430070
• Online:2020-03-25 Published:2020-05-30

Regularized ranking algorithm based on kernels has recently gained much attention in machine learning theory, and pairwise learning is the generalization of ranking problem. In this paper, a kernel-based regularized pairwise learning algorithm with a quasiconvex loss function is provided, the error estimate is given by using the quasiconvex analysis theory, and an explicit learning rate is obtained. It is shown that the sample error is influenced by the parameters in the loss function. The experiments show that our method is more robust compared with the ranking algorithm with the least square loss function.

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