Positive Characteristic Sets for Relational Pattern Languages

TL;DR

This paper introduces the concept of positive characteristic sets for relational pattern languages, focusing on learning from positive examples in string processing applications.

Contribution

It defines and studies positive characteristic sets specifically for relational pattern languages, advancing understanding in learning from positive data only.

Findings

Introduces positive characteristic sets for relational pattern languages

Highlights importance for learning from positive examples only

Applicable to string processing tasks

Abstract

In the context of learning formal languages, data about an unknown target language L is given in terms of a set of (word,label) pairs, where a binary label indicates whether or not the given word belongs to L. A (polynomial-size) characteristic set for L, with respect to a reference class L of languages, is a set of such pairs that satisfies certain conditions allowing a learning algorithm to (efficiently) identify L within L. In this paper, we introduce the notion of positive characteristic set, referring to characteristic sets of only positive examples. These are of importance in the context of learning from positive examples only. We study this notion for classes of relational pattern languages, which are of relevance to various applications in string processing.