论文标题
关于具有独立和非相同分布元素的特征向量的监督分类
On Supervised Classification of Feature Vectors with Independent and Non-Identically Distributed Elements
论文作者
论文摘要
在本文中，我们研究了将特征向量与相互独立但非相同分布的元素分类的问题。首先，我们表明了这个问题的重要性。接下来，我们提出一个分类器，并在其误差概率上得出一个分析上限。我们表明，随着特征向量的长度的增长，即使每个标签只有一个训练功能向量，误差概率也会零。因此，我们表明，对于这个重要问题，至少存在一个渐近最佳分类器。最后，我们提供数值示例，其中我们表明，当训练数据的数量较小并且特征向量的长度足够高时，提出的分类器的性能优于常规分类算法。
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.
