Concept Class

In computational learning theory in mathematics, a concept over a domain ''X'' is a total Boolean function over ''X''. A concept class is a class of concepts. Concept classes are a subject of computational learning theory.

Concept class terminology frequently appears in model theory associated with probably approximately correct (PAC) learning.Chase, H., & Freitag, J. (2018). ''Model Theory and Machine Learning''. arXiv preprint arXiv:1801.06566
In this setting, if one takes a set ''Y'' as a set of (classifier output) labels, and ''X'' is a set of examples, the map $c: X\to Y$, i.e. from examples to classifier labels (where $Y = \$ and where ''c'' is a subset of ''X''), ''c'' is then said to be a ''concept''. A ''concept class'' $C$ is then a collection of such concepts.

Given a class of concepts ''C'', a subclass ''D'' is reachable if there exists a sample ''s'' such that ''D'' contains exactly those concepts in ''C'' that are extensions to ''s''. Not every subclass is reachable.

Background

A ''sample'' $s$ is a partial function from $X$ to $\$. Identifying a concept with its characteristic function mapping $X$ to $\$, it is a special case of a sample.

Two samples are ''consistent'' if they agree on the intersection of their domains. A sample $s'$ ''extends'' another sample $s$ if the two are consistent and the domain of $s$ is contained in the domain of $s'$.

Examples

Suppose that $C = S^+(X)$. Then:

* the subclass $\$ is reachable with the sample $s = \$;
* the subclass $S^+(Y)$ for $Y\subseteq X$ are reachable with a sample that maps the elements of $X - Y$ to zero;
* the subclass $S(X)$, which consists of the singleton sets, is ''not'' reachable.

Applications

Let $C$ be some concept class. For any concept $c\in C$, we call this concept $1/d$''-good'' for a positive integer $d$ if, for all $x\in X$, at least $1/d$ of the concepts in $C$ agree with $c$ on the classification of $x$. The ''fingerprint dimension'' $FD(C)$ of the entire concept class $C$ is the least positive integer $d$ such that every reachable subclass $C'\subseteq C$ contains a concept that is $1/d$-good for it. This quantity can be used to bound the minimum number of equivalence queries needed to learn a class of concepts according to the following inequality:$FD(C) - 1\leq \#EQ(C)\leq \lceil FD(C)\ln(, C, )\rceil$.

References

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Computational learning theory