论文标题
分类协变量应用于二进制分类问题的布尔功能的概率学习
Probabilistic learning of boolean functions applied to the binary classification problem with categorical covariates
论文作者
论文摘要
在这项工作中，我们将二进制分类的问题提出了估计Bernoulli数据的分区。当解释变量都是分类时，可以使用布尔函数的语言对问题进行建模。我们对问题进行了概率分析，并提出了两种从二进制数据中学习布尔功能的算法。
In this work we cast the problem of binary classification in terms of estimating a partition on Bernoulli data. When the explanatory variables are all categorical, the problem can be modelled using the language of boolean functions. We offer a probabilistic analysis of the problem, and propose two algorithms for learning boolean functions from binary data.
