Error Analysis of ERM Algorithm with Unbounded and Non-Identical Sampling
Error Analysis of ERM Algorithm with Unbounded and Non-Identical Sampling
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DOI：
10.4236/jamp.2016.41019
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发表时间：
2016-01
期刊：
影响因子：
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通讯作者：
Weilin Nie;Cheng Wang
中科院分区：
文献类型：
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作者：
Weilin Nie;Cheng Wang
学习理论文献中的一个标准假设是样本独立地从具有一致有界输出的相同分布中抽取。这排除了高斯分布的常见情况。在本文中，我们将这些假设推广到一般情况。准确地说，样本是从一系列无界和不相同的概率分布中抽取的。通过漂移误差分析和无界随机变量的班尼特不等式，我们得到了一个令人满意的学习率的ERM算法。
A standard assumption in the literature of learning theory is the samples which are drawn independently from an identical distribution with a uniform bounded output. This excludes the common case with Gaussian distribution. In this paper we extend these assumptions to a general case. To be precise, samples are drawn from a sequence of unbounded and non-identical probability distributions. By drift error analysis and Bennett inequality for the unbounded random variables, we derive a satisfactory learning rate for the ERM algorithm.