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Transformational machine learning: Learning how to learn from many related scientific problems.
Olier, Ivan; Orhobor, Oghenejokpeme I; Dash, Tirtharaj; Davis, Andy M; Soldatova, Larisa N; Vanschoren, Joaquin; King, Ross D.
Affiliation
  • Olier I; School of Computer Science and Mathematics, Liverpool John Moores University, Liverpool L3 5UX, United Kingdom.
  • Orhobor OI; Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge CB3 0AS, United Kingdom.
  • Dash T; Anuradha and Prashanth Palakurthi Centre for Artificial Intelligence Research, Department of Computer Science & Information Systems, Birla Institute of Technology and Science Pilani, Goa 403726, India.
  • Davis AM; Discovery Sciences, Biopharmaceuticals R&D, AstraZeneca, Cambridge CB4 0WG, United Kingdom.
  • Soldatova LN; Department of Computing, Goldsmiths, University of London, London SE14 6NW, United Kingdom.
  • Vanschoren J; Department of Mathematics and Computer Science, Eindhoven University of Technology 5612 AZ Eindhoven, The Netherlands.
  • King RD; Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge CB3 0AS, United Kingdom; rk663@cam.ac.uk.
Proc Natl Acad Sci U S A ; 118(49)2021 12 07.
Article in En | MEDLINE | ID: mdl-34845013
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Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Prognostic_studies Language: En Journal: Proc Natl Acad Sci U S A Year: 2021 Document type: Article Affiliation country: United kingdom
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Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Prognostic_studies Language: En Journal: Proc Natl Acad Sci U S A Year: 2021 Document type: Article Affiliation country: United kingdom