Shattering: Difference between revisions

From Machinelearning
(Created page with "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\} \subset...")
 
No edit summary
Line 1: Line 1:
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 X be a set of objects one wants to label as 0 or 1, and let H be a class of functions from X to {0,1}, called a hypothesis class. Let S={p1,,pn}X be a set of points in X. The hypothesis class H shatters S iff for every label assignment f:S{0,1} there exists some hH such that h(p)=f(p) for all pS.

We can use the idea of function restriction to restate this idea:

The hypothesis class H shatters S iff for every label assignment f:S{0,1} there exists some hH such that h|S=f.

The hypothesis class H shatters S iff {h|S:hH}={0,1}S.

Since functions going into {0,1} can be seen as subsets of the domain, we can also state the definition using just sets. Let H'={h1({1}):hH} be the set of possible regions that the hypothesis class can label as positive.

The hypothesis class H shatters S iff for each subset E of S there exists some set HH such that E=HS.

The hypothesis class H shatters S iff P(S)={HS:HH}.

References

https://en.wikipedia.org/wiki/Shattered_set

Understanding Machine Learning, chapter on VC dimension.