Title

Concept Learning By Example Decomposition

Keywords

Computational learning theory; Concept decomposition; Concept learning; Example decomposition; Learning by decomposition; Probably approximately correct

Abstract

It is widely accepted in machine learning that it is easier to learn several smaller decomposed concepts than a single large one. Typically, such decomposition of concepts is achieved in highly constrained environments, or aided by human experts. In this article, we investigate concept learning by example decomposition in a general probably approximately correct setting for Boolean learning. We develop sample complexity bounds for the different steps involved in the process. We formally show that if the cost of example partitioning is kept low then it is highly advantageous to learn by example decomposition.

Publication Date

3-1-2010

Publication Title

Journal of Experimental and Theoretical Artificial Intelligence

Volume

22

Issue

1

Number of Pages

1-21

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1080/09528130802386051

Socpus ID

77951187847 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/77951187847

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