V
Vincent Lemaire
Researcher at Orange S.A.
Publications - 105
Citations - 1209
Vincent Lemaire is an academic researcher from Orange S.A.. The author has contributed to research in topics: Supervised learning & Computer science. The author has an hindex of 16, co-authored 94 publications receiving 1059 citations.
Papers
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Journal ArticleDOI
Open challenges for data stream mining research
Georg Krempl,Indre Žliobaite,Dariusz Brzezinski,Eyke Hüllermeier,Vincent Lemaire,Tino Noack,Ammar Shaker,Sonja Sievi,Myra Spiliopoulou,Jerzy Stefanowski +9 more
TL;DR: This article presents a discussion on eight open challenges for data stream mining, which cover the full cycle of knowledge discovery and involve such problems as protecting data privacy, dealing with legacy systems, handling incomplete and delayed information, analysis of complex data, and evaluation of stream mining algorithms.
Book Chapter
Results of the Active Learning Challenge
TL;DR: The design of the challenge and its results are summarized in this paper and the best contributions made by the participants are included in these proceedings.
Proceedings Article
Analysis of the KDD cup 2009: fast scoring on a large orange customer database
TL;DR: The KDD Cup 2009 as mentioned in this paper focused on identifying data mining techniques capable of rapidly building predictive models and scoring new entries on a large CRM database, and the results of the challenge were discussed at the KDD conference (June 28, 2009).
Book ChapterDOI
A Survey on Supervised Classification on Data Streams
TL;DR: This article presents the main approaches of incremental supervised classification available in the literature and aims to give basic knowledge to a reader novice in this subject.
Journal ArticleDOI
Optimised probabilistic active learning (OPAL)
TL;DR: This work proposes a fast, non-myopic, and cost-sensitive probabilistic active learning approach for binary classification, and derives and uses a closed-form solution for the expected reduction in misclassification loss in a labelling candidate’s neighbourhood.