独立非同分布特征向量的监督分类
On Supervised Classification of Feature Vectors with Independent and Non-Identically Distributed Elements
摘要：
在本文中，我们研究的问题，相互独立，但不同分布的元素的特征向量的分类。首先，我们要说明这个问题的重要性。接下来，我们提出了一个分类器，并推导出其错误概率的分析上限。我们表明，随着特征向量长度的增长，错误概率趋于零，即使每个标签只有一个训练特征向量。因此，我们表明，对于这个重要的问题，至少存在一个渐近最优分类器。最后，我们提供了数值例子，我们表明，所提出的分类器的性能优于传统的分类算法时，训练数据的数量是小的，特征向量的长度是足够高的。
英文摘要：
In this paper, we investigate the problem of classifying feature vectors with mutually independent but non-identically distributed elements. First, we show the importance of this problem. Next, we propose a classifier and derive an analytical upper bound on its error probability. We show that the error probability goes to zero as the length of the feature vectors grows, even when there is only one training feature vector per label available. Thereby, we show that for this important problem at least one asymptotically optimal classifier exists. Finally, we provide numerical examples where we show that the performance of the proposed classifier outperforms conventional classification algorithms when the number of training data is small and the length of the feature vectors is sufficiently high.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
