基于独立但不同分布样本的系数正则化回归算法的研究

Analysis of Coefficient Regularized Regression with Non-Identically and Independently Sampling

摘要: 本文分析了基于独立但不同分布样本的系数正则化回归学习算法。全文的框架不同于以往的经典核学习方法，核函数不再要求满足半正定性；对于样本输出，令其满足弱化的矩假设条件。文章使用积分算子的方法得到了满意的与容量无关的误差界，最后通过选取合适的正则化参数得到较为满意的学习率。

Abstract: This paper considers the error analysis of coefficient regularization with non-identically and independently sampling. The framework under investigation is different from classical kernel learning. The kernel function no longer satisfies the positive semidefiniteness; we carry out the error analysis with output sample values satisfying a generalized moment hypothesis. Satisfactory capacity independently error bounds are derived by the techniques of integral operator for this learning algorithm, finally, a satisfactory learning rate is obtained by selecting appropriate regularization parameters.