Advances in Knowledge Discovery and Management (10): Volume 10 - Rilegato

Rilegato

ISBN 10: 3031404025 ISBN 13: 9783031404023

Casa editrice: Springer Nature, 2024

Riassunto

This book comprises a distinguished collection of cutting-edge scientific contributions. Encompassing a wide range of subjects, it delves into machine learning, data mining, text analysis, data visualization, knowledge management, and more. The included articles are expanded versions of carefully selected top papers that were originally presented at the EGC’2020 conferences held in Paris (France, January 27-31, 2020).  It is intended for researchers interested in these fields, including PhD and MSc students, and researchers from public or private laboratories. These extended versions underwent an additional peer-review process, building upon the already accepted long-format papers from the conference. The selection of long and short papers for the conference itself followed a rigorous double-blind peer-review process, evaluating numerous submissions (with a long paper acceptance rate of approximately 25%). For more details about the EGC society, please consult egc.asso.fr."

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Informazioni sull?autore

Antoine Cornuejols is Professor in Computer Science at AgroParisTech, University
of Paris-Saclay. He is head of the Ekinocs research team and co-director of the
H@rvest chair on the role of data sciences in agriculture. He has published a largenumber of research articles in major journals and conferences and is co-author of
two books: one on Machine Learning’s concepts and algorithms (in French, 4th edition)
and one on Phase Transitions in Machine Learning. He has been the general
chair and program chair of several conferences, noticeably of EGC-2020 in Brussels.
He has been thinking and working on artificial intelligence and machine learning since his doctorate studies at UCLA and Orsay University, from which he graduated.
He is specifically interested in on line learning, transfer learning and collaborative
learning, settings where the classical machine learning approach based on the assumption
of a stationary environment must yield to new principles.

Etienne Cuvelier is an assistant professor at ICHEC Brussels Management
School (Brussels, Belgium), a trainer at CentraleSupélec Executive Education (Paris,
France) and co-founder of the QUARESMI laboratory (Brussels, Belgium). After a
degree in mathematics and a degree in computer science at UMons (Mons, Belgium),he started a first career in secondary, higher and social education. Etienne
Cuvelier then completed a PhD in computer science at the Faculty of Computer Science
of UNamur (Namur, Belgium) in 2009. His main research interests are in the
area of complex data analysis (functional data, symbolic data, graphs).

Arnaud Martin is full professor at University of Rennes 1 in the team DRUID
of IRISA laboratory. He received a HDR (French ability to supervised research) in
computer sciences (2009), a PhD degree in Signal Processing (2001), and Master
in Probability (1998). Pr. Arnaud Martin joined the laboratory IRISA at the universityof Rennes 1 as full professor in 2010 and co-create the team DRUID in 2012.
He teaches data fusion, data mining, and computer sciences. His research interests
are mainly related to the belief functions with applications on social networks and
crowdsourcing. He is author of numerous papers and invited talks. He supervised
numerous Phd students.

Rakia Jaziriis currently associate professor in computer science at Paris 8 University, researcher

in the Artificial Intelligence Laboratory of Saint Denis (Paragraphe) and head of master program in Big Data and data mining. She is a former student of the Ecole Centrale de Lyon, she received the PhD degree in Computer Science in 2013 from the University of Paris Sorbonne in collaboration with the National Audiovisual Institute, then
a post-doctorate in the field of Artificial Intelligence on massive data in particular,
on algorithmsfor detecting aberrant data (suspicious behavior), the discovery of the typology of trajectories, and the prediction of the behavior of Internet users. 

As a member of the program committees for major conferences in her field, she has regularly
organized French-speaking conferences around AI. Her research topics now focus on machine learning. She is interested in analyzing data from social networks to extract information deemed to be significant for decision support. Her work has been the subject of numerous international publications and she was able to form partnerships with industry from all over the world.

Fabrice Guillet is a full professor in CS at Polytech’Nantes, the graduate engineering
school of University of Nantes, France, and a member of the ” Data User Knowledge”
team (DUKe) of the LS2N laboratory. He received a PhD degree in CS in 1995
from the ” École Nationale Supérieure des Télécommunications de Bretagne”, andhis Habilitation (HdR) in 2006 from Nantes university. He is a co-founder and vicepresident
of the International French-speaking “Extraction et Gestion des
Connaissances (EGC)” society. His research interests include knowledge quality
and knowledge visualization in the frameworks of Data Science and Knowledge
Management. He has co-edited two refereed books of chapter entitled “QualityMeasures in Data Min-ing” and ” Statistical Implicative Analysis — Theory and
Applications” published by Springer in 2007 and 2008.

Dettagli dell?edizione

• Editore: Springer Nature
• Data di pubblicazione: 2024
• Lingua: Inglese
• ISBN 10: 3031404025
• ISBN 13: 9783031404023
• Rilegatura: Copertina rigida
• Numero edizione: 1
• Numero di pagine: 148
• Redattore: Jaziri Rakia, Martin Arnaud, Cornujols Antoine, Cuvelier Etienne, Guillet Fabrice
• Contatto del produttore: non disponibile
• Persona responsabile: CMN Printpool Inh. Mathis Neumann; https://www.also.com/ec/cms5/en_6000/6000/index.jsp; ; Friedrich-Karl-Str. 9; Hamburg; Germania