Concept learning by example decomposition
Abbreviated Journal Title
J. Exp. Theor. Artif. Intell.
concept learning; example decomposition; concept decomposition; probably; approximately correct; computational learning theory; learning by; decomposition; FEATURE-EXTRACTION; MODEL; Computer Science, Artificial Intelligence
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.
Journal of Experimental & Theoretical Artificial Intelligence
"Concept learning by example decomposition" (2010). Faculty Bibliography 2010s. 316.