Abstract
AbstractIn the last decade, genetics‐based machine learning methods have shown their competence in large‐scale data mining tasks because of the scalability capacity that these techniques have demonstrated. This capacity goes beyond the innate massive parallelism of evolutionary computation methods by the proposal of a variety of mechanisms specifically tailored for machine learning tasks, including knowledge representations that exploit regularities in the datasets, hardware accelerations or data‐intensive computing methods, among others. This paper reviews different classes of methods that alone or (in many cases) combined accelerate genetics‐based machine learning methods. © 2013 Wiley Periodicals, Inc.This article is categorized under: Technologies > Classification Technologies > Computational Intelligence
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
