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Growth function
The growth function, also called the shatter coefficient or the shattering number, measures the richness of a set family or class of functions. It is especially used in the context of statistical learning theory, where it is used to study properties of statistical learning methods. 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 :
1. The domain is the real line . The set-family contains all the half-lines (rays) from a given number to positive infinity, i.e., all sets of the form for some . For any set of real numbers, the intersection contains sets: the empty set, the set containing the largest element of , the set containing the two largest elements of , and so on. Therefore: . The same is true whether contains open half-lines, closed half-lines, or both.
2. The domain is the segment . The set-family contains all the open sets. For any finite set of real numbers, the intersection contains all possible subsets of . There are such subsets, so .
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Growth function
The growth function, also called the shatter coefficient or the shattering number, measures the richness of a set family or class of functions. It is especially used in the context of statistical learning theory, where it is used to study properties of statistical learning methods. 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 :
1. The domain is the real line . The set-family contains all the half-lines (rays) from a given number to positive infinity, i.e., all sets of the form for some . For any set of real numbers, the intersection contains sets: the empty set, the set containing the largest element of , the set containing the two largest elements of , and so on. Therefore: . The same is true whether contains open half-lines, closed half-lines, or both.
2. The domain is the segment . The set-family contains all the open sets. For any finite set of real numbers, the intersection contains all possible subsets of . There are such subsets, so .