Stochastic classification models
Stochastic classification models
复制标题
随机分类模型
DOI：
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发表时间：
2006
期刊：
影响因子：
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通讯作者：
Jie Yang
中科院分区：
文献类型：
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作者：
Peter McCullagh;Jie Yang
构造了两类随机过程，它们旨在用于
分类问题，其目的是根据以下条件对单位或标本或物种进行分类
测量的特征。第一个模型是由标准生成的可交换集群过程
Dirichlet分配方案。这组类没有预先指定，因此可能会分配一个新单元
归入一个以前未被观察到的阶层。第二种模型更灵活，它使用了一个标记点
作为生成单元或事件的机制的过程，每个单元或事件都有其关联的类和特征。
给定叠加过程的条件分布是以封闭形式得到的
特殊标记点过程。该分布确定条件类概率，
从而为后续单元制定了预测规则。
Two families of stochastic processes are constructed that are intended for use in
classification problems where the aim is to classify units or specimens or species on the basis of
measured features. The first model is an exchangeable cluster process generated by a standard
Dirichlet allocation scheme. The set of classes is not pre-specified, so a newunit may be assigned
to a previously unobserved class. The second model, which is more flexible, uses a marked point
process as the mechanism generating the units or events, each with its associated class and feature.
The conditional distribution given the superposition process is obtained in closed form for one
particular marked point process. This distribution determines the conditional class probabilities,
and thus the prediction rule for subsequent units.