Nataf-Beta随机场分类器：Beta共轭先验分类问题的推广
The Nataf-Beta Random Field Classifier: An Extension of the Beta Conjugate Prior to Classification Problems
摘要：
本文介绍了Nataf Beta随机场分类器，判别方法，扩展了适用性的Beta共轭分类问题之前。该方法的主要特点是将一类属性值的概率建模为一个随机场，其边缘是Beta分布的，并且边缘的参数本身由随机场描述。虽然所提出的方法的分类精度在统计上并没有超过文献中报道的最佳精度，但它在测试的六个基准数据集中名列前茅。Nataf-Beta随机场分类器适合作为实连续和实整数属性值问题的通用分类方法。
英文摘要：
This paper presents the Nataf-Beta Random Field Classifier, a discriminative approach that extends the applicability of the Beta conjugate prior to classification problems. The approach's key feature is to model the probability of a class conditional on attribute values as a random field whose marginals are Beta distributed, and where the parameters of marginals are themselves described by random fields. Although the classification accuracy of the approach proposed does not statistically outperform the best accuracies reported in the literature, it ranks among the top tier for the six benchmark datasets tested. The Nataf-Beta Random Field Classifier is suited as a general purpose classification approach for real-continuous and real-integer attribute value problems.
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。
