Feb. 12, 2024, 5:43 a.m. | Andrew Ni Li Ding Lee Spector

cs.LG updates on arXiv.org arxiv.org

Lexicase selection has been shown to provide advantages over other selection algorithms in several areas of evolutionary computation and machine learning. In its standard form, lexicase selection filters a population or other collection based on randomly ordered training cases that are considered one at a time. This iterated filtering process can be time-consuming, particularly in settings with large numbers of training cases. In this paper, we propose a new method that is nearly equivalent to lexicase selection in terms of …

advantages aggregation algorithms cases collection computation cs.lg cs.ne diverse filtering filters form machine machine learning population process standard training via

Doctoral Researcher (m/f/div) in Automated Processing of Bioimages

@ Leibniz Institute for Natural Product Research and Infection Biology (Leibniz-HKI) | Jena

Research Scholar (Technical Research)

@ Centre for the Governance of AI | Hybrid; Oxford, UK

Lead Software Engineer, Machine Learning

@ Monarch Money | Remote (US)

Investigator, Data Science

@ GSK | Stevenage

Alternance - Assistant.e Chef de Projet Data Business Intelligence (H/F)

@ Pernod Ricard | FR - Paris - The Island

Expert produit Big Data & Data Science - Services Publics - Nantes

@ Sopra Steria | Nantes, France