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--- students:phd_mlws [2017/05/28 17:39] – [Objectives] blay
+++ students:phd_mlws [2017/05/28 18:03] (current) – [Context] blay
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          * The structural characteristics (size, quality, and nature) of the collected data
          * How the results will be used.
-This task is highly complex because of the increasing number of available algorithms, the difficulty in choosing the correct preprocessing techniques together with the right algorithms as well as the correct tuning of their parameters. To decide which algorithm to choose, data scientists often consider families of algorithms in which they are experts, and can leave aside algorithms that are more “exotic” to them, but could perform better for the problem they are trying to solve.
+This task is highly complex because of the increasing number of available algorithms, the difficulty in choosing the correct preprocessing techniques together with the right algorithms as well as the correct tuning of their parameters (Serban at al, 2013). To decide which algorithm to choose, data scientists often consider families of algorithms in which they are experts, and can leave aside algorithms that are more “exotic” to them, but could perform better for the problem they are trying to solve.
 ROCKFlows  is a project aiming at helping users to create their own Machine Learning Workflows by simply describing their dataset and objectives.
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 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
+Serban F, Vanschoren J, Kietz J-U, Bernstein A (2013) A survey of intelligent assistants for data analysis. ACM Comput Surv. doi: 10.1145/2480741.2480748
 Wolpert D (1996) The lack of a priori distinctions between learning algorithms. Neural Computation 8(7):1341–1390