Skip to Main content Skip to Navigation
Conference papers

Evaluation of Lifelong Learning Systems

Abstract : Current intelligent systems need the expensive support of machine learning experts to sustain their performance level when used on a daily basis. To reduce this cost, i.e. remaining free from any machine learning expert, it is reasonable to implement lifelong (or continuous) learning intelligent systems that will continuously adapt their model when facing changing execution conditions. In this work, the systems are allowed to refer to human domain experts who can provide the system with relevant knowledge about the task. Nowadays, the fast growth of lifelong learning systems development rises the question of their evaluation. In this article we propose a generic evaluation methodology for the specific case of lifelong learning systems. Two steps will be considered. First, the evaluation of human-assisted learning (including active and/or interactive learning) outside the context of lifelong learning. Second, the system evaluation across time, with propositions of how a lifelong learning intelligent system should be evaluated when including human assisted learning or not.
Document type :
Conference papers
Complete list of metadata

Cited literature [15 references]  Display  Hide  Download
Contributor : anthony larcher Connect in order to contact the contributor
Submitted on : Tuesday, March 3, 2020 - 1:44:16 PM
Last modification on : Tuesday, May 17, 2022 - 12:32:01 PM
Long-term archiving on: : Thursday, June 4, 2020 - 3:37:05 PM


Files produced by the author(s)


  • HAL Id : hal-02496971, version 1



yevhenii Prokopalo, Sylvain Meignier, Olivier Galibert, Loïc Barrault, Anthony Larcher. Evaluation of Lifelong Learning Systems. International Conference on Language Resources and Evaluation, May 2020, Marseille, France. ⟨hal-02496971⟩



Record views


Files downloads