论文标题
关闭操作员：分类和决策的复杂性和应用
Closure operators: Complexity and applications to classification and decision-making
论文作者
论文摘要
我们研究了关闭操作员的复杂性，并应用了机器学习和决策理论。在机器学习中，闭合操作员在数据分类和聚类中自然出现。在决策理论中，它们可以模拟选择菜单的等效性，因此可以偏爱灵活性。我们的贡献是制定闭合操作员复杂性的概念，该概念转化为ML中分类器的复杂性或决策理论中的效用函数。
We study the complexity of closure operators, with applications to machine learning and decision theory. In machine learning, closure operators emerge naturally in data classification and clustering. In decision theory, they can model equivalence of choice menus, and therefore situations with a preference for flexibility. Our contribution is to formulate a notion of complexity of closure operators, which translate into the complexity of a classifier in ML, or of a utility function in decision theory.
