Normal Discrimination with Unclassified Observations
Normal Discrimination with Unclassified Observations
复制标题
未分类观察的正常歧视
DOI：
10.1080/01621459.1978.10480106
复制
发表时间：
1978
影响因子：
3.7
通讯作者：
Terence J. O'Neill
中科院分区：
文献类型：
--
作者：
Terence J. O'Neill
摘要 Fisher 线性判别规则可以通过使用未分类观测值的最大似然估计来估计。结果表明，对于统计上有趣的群体分离范围，非分类观测中包含的相关信息与分类观测中包含的相关信息的比率从大约五分之一到三分之二变化。因此，从大量廉价的未分类观察中可以获得比从少量分类样本中获得更多的信息。此外，所有可用的未分类和分类数据都应用于估计费舍尔线性判别规则。
Abstract Fisher's linear discriminant rule may be estimated by maximum likelihood estimation using unclassified observations. It is shown that the ratio of the relevant information contained in unclassified observations to that in classified observations varies from approximately one-fifth to two-thirds for the statistically interesting range of separation of the populations. Thus, more information may be obtained from large numbers of inexpensive unclassified observations than from a small classified sample. Also, all available unclassified and classified data should be used for estimating Fisher's linear discriminant rule.