The growth function, also called the shatter coefficient or the shattering number, measures the richness of a set family. It is especially used in the context of statistical learning theory, where it measures the complexity of a hypothesis class. The term 'growth function' was coined by Vapnik and Chervonenkis in their 1968 paper, where they also proved many of its properties. It is a basic concept in machine learning. Let be a set family (a set of sets) and a set. Their intersection is defined as the following set-family: The intersection-size (also called the index) of with respect to is . If a set has elements then the index is at most . If the index is exactly 2m then the set is said to be shattered by , because contains all the subsets of , i.e.: The growth function measures the size of as a function of . Formally: Equivalently, let be a hypothesis-class (a set of binary functions) and a set with elements. The restriction of to is the set of binary functions on that can be derived from : The growth function measures the size of as a function of :