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--- students:phd_mlws [2017/05/20 21:08] – [Objectives] blay
+++ students:phd_mlws [2017/05/28 17:37] – [Bibliographie] blay
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 ====== Machine Learning Workflow System ======
-This subject is proposed as part of the [[http://rockflows.i3s.unice.fr/|ROCKFlows]] project involving the following researchers: [[http://mireilleblayfornarino.i3s.unice.fr|Mireille Blay-Fornarino]], [[http://www.i3s.unice.fr/~mosser/start|Sébastien Mosser]] and [[http://www.i3s.unice.fr/~precioso/|Frédéric Precioso]].
+This subject is proposed as part of the [[http://rockflows.i3s.unice.fr/|ROCKFlows]] project.
 ===== Context =====
 For many years, Machine Learning research has been focusing on designing new algorithms for solving similar kinds of problem instances (Kotthoff, 2016). However, Researchers have long ago recognized that a single algorithm will not give the best performance across all problem instances, e.g. the No-Free-Lunch-Theorem (Wolpert, 1996) states that the best classifier will not be the same on every dataset. Consequently, the “winner-take-all” approach should not lead to neglect some algorithms that, while uncompetitive on average, may offer excellent performances on particular problem instances. In 1976, Rice characterized this as the "algorithm selection problem" (Rice, 1976).
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 Rice JR (1976) The Algorithm Selection Problem. Adv Comput 15:65–118.
+Martin Salvador M, Budka M, Gabrys B (2016) Towards automatic composition of multicomponent predictive systems. Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinformatics). doi: 10.1007/978-3-319-32034-2_3
 Wolpert D (1996) The lack of a priori distinctions between learning algorithms. Neural Computation 8(7):1341–1390