Shattering: Difference between revisions
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Let <math>\mathcal H</math> be a class of functions from <math>\mathcal X</math> to <math>\{0,1\}</math>, called a hypothesis class. Let <math>S = \{p_1, \ldots, p_n\} \subseteq \mathcal X</math> be a set of points in <math>\mathcal X</math>. The hypothesis class <math>\mathcal H</math> shatters <math>S</math> iff for every label assignment <math>f : S \to \{0,1\}</math> there exists some <math>h \in \mathcal H</math> such that <math>h(p) = f(p)</math> for all <math>p \in S</math>. | Let <math>\mathcal X</math> be a set of objects one wants to label as 0 or 1, and let <math>\mathcal H</math> be a class of functions from <math>\mathcal X</math> to <math>\{0,1\}</math>, called a hypothesis class. Let <math>S = \{p_1, \ldots, p_n\} \subseteq \mathcal X</math> be a set of points in <math>\mathcal X</math>. The hypothesis class <math>\mathcal H</math> shatters <math>S</math> iff for every label assignment <math>f : S \to \{0,1\}</math> there exists some <math>h \in \mathcal H</math> such that <math>h(p) = f(p)</math> for all <math>p \in S</math>. | ||
We can use the idea of function restriction to restate this idea: | We can use the idea of function restriction to restate this idea: | ||
Revision as of 20:01, 15 July 2019
Let be a set of objects one wants to label as 0 or 1, and let be a class of functions from to , called a hypothesis class. Let be a set of points in . The hypothesis class shatters iff for every label assignment there exists some such that for all .
We can use the idea of function restriction to restate this idea:
The hypothesis class shatters iff for every label assignment there exists some such that .
The hypothesis class shatters iff .
Since functions going into can be seen as subsets of the domain, we can also state the definition using just sets. Let be the set of possible regions that the hypothesis class can label as positive.
The hypothesis class shatters iff for each subset of there exists some set such that .
The hypothesis class shatters iff .
References
https://en.wikipedia.org/wiki/Shattered_set
Understanding Machine Learning, chapter on VC dimension.