hierarchical softmax
时间: 2023-04-24 14:02:36 浏览: 81
Hierarchical softmax是一种在语言模型中计算单词概率的优化方法。它通过将单词表示为一棵二叉树,每个单词对应二叉树上的一个叶子节点,并从根节点开始递归地计算概率。这种方法能够大大减少计算量,提高计算效率。
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Hierarchical models
Hierarchical models are statistical models that allow for different levels of variation in a dataset to be modeled and analyzed. These models are useful when data is collected from multiple sources or when there are multiple levels of variation in the data.
In a hierarchical model, the data is organized into multiple levels, with each level representing a different source of variation. The model estimates the parameters at each level, allowing for the analysis of the data at each level separately.
For example, in a study of student performance, a hierarchical model might have a level for individual student characteristics, a level for classroom characteristics, and a level for school characteristics. By analyzing the data at each level, the model can provide insights into the factors that influence student performance and how these factors interact with each other.
Hierarchical models are widely used in a variety of fields, including psychology, education, public health, and economics. They can be implemented using a variety of statistical techniques, including Bayesian modeling, mixed-effects modeling, and hierarchical linear modeling.
hierarchical clustering
层次聚类(Hierarchical Clustering)是一种聚类算法,它通过计算不同类别数据点间的相似度来创建一棵有层次的嵌套聚类树,距离越小,相似度越高。在聚类树中,不同类别的原始数据点是树的最低层,树的顶层是一个聚类的根节点。创建聚类树有自下而上合并和自上而下分裂两种方法,这两种方法分别称为聚类与分裂。其中,自下向上的聚类方法(凝聚分层聚类)的算法思想是将每个点都看成一个簇,将两个最近的簇合并为一个簇,不断重复上述过程,直到达到预期簇或簇之间的距离满足要求为止(指定簇数或样本距离阈值)。