Reference : The Top 10 Topics in Machine Learning Revisited: A Quantitative Meta-Study

	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Computer science
	Focus Areas :	Computational Sciences
	To cite this reference:	http://hdl.handle.net/10993/30443

Title :	The Top 10 Topics in Machine Learning Revisited: A Quantitative Meta-Study
Language :	English
Author, co-author :	Glauner, Patrick [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Du, Manxing [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Paraschiv, Victor [Numbers of others]
	Boytsov, Andrey [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Lopez Andrade, Isabel [American Express]
	Meira, Jorge Augusto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Valtchev, Petko [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	State, Radu [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Publication date :	2017
Main document title :	Proceedings of the 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2017)
Peer reviewed :	Yes
Audience :	International
ISBN :	9782875870391
Event name :	25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2017)
Event date :	from 26-04-2017 to 28-04-2017
Abstract :	[en] Which topics of machine learning are most commonly addressed in research? This question was initially answered in 2007 by doing a qualitative survey among distinguished researchers. In our study, we revisit this question from a quantitative perspective. Concretely, we collect 54K abstracts of papers published between 2007 and 2016 in leading machine learning journals and conferences. We then use machine learning in order to determine the top 10 topics in machine learning. We not only include models, but provide a holistic view across optimization, data, features, etc. This quantitative approach allows reducing the bias of surveys. It reveals new and up-to-date insights into what the 10 most prolific topics in machine learning research are. This allows researchers to identify popular topics as well as new and rising topics for their research.
Permalink :	http://hdl.handle.net/10993/30443

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