Year: 2020
Type: preprint
Abstract: We present an extension to active learning routines in which non-constant costs are explicitly considered. This work considers both known and unknown costs and introduces the term ε-frugal for learners that do not only consider minimizing total costs but are also able to explore high cost regions of the sample space. We demonstrate our extension on a well-known machine learning dataset and find that out ε-frugal learners outperform both learners with known costs and random sampling.
Source: arXiv (Cornell University)
Authors: Stillman, Namid, Balazs, Igor, Hauert, Sabine
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3092431376