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Mathematical optimization methods for the analysis of compositional data: subset selection, unmixing and prediction

Jan Verwaeren (UGent)
(2014)
Author
Promoter
(UGent)
Organization
Keywords
machine learning, unmixing, subset selection, mathematical optimization, compositional data

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Citation

Please use this url to cite or link to this publication:

Chicago
Verwaeren, Jan. 2014. “Mathematical Optimization Methods for the Analysis of Compositional Data: Subset Selection, Unmixing and Prediction”. Ghent, Belgium: Ghent University. Faculty of Bioscience Engineering.
APA
Verwaeren, J. (2014). Mathematical optimization methods for the analysis of compositional data: subset selection, unmixing and prediction. Ghent University. Faculty of Bioscience Engineering, Ghent, Belgium.
Vancouver
1.
Verwaeren J. Mathematical optimization methods for the analysis of compositional data: subset selection, unmixing and prediction. [Ghent, Belgium]: Ghent University. Faculty of Bioscience Engineering; 2014.
MLA
Verwaeren, Jan. “Mathematical Optimization Methods for the Analysis of Compositional Data: Subset Selection, Unmixing and Prediction.” 2014 : n. pag. Print.
@phdthesis{4418612,
  author       = {Verwaeren, Jan},
  isbn         = {9789059897113},
  keyword      = {machine learning,unmixing,subset selection,mathematical optimization,compositional data},
  language     = {eng},
  pages        = {XV, 322},
  publisher    = {Ghent University. Faculty of Bioscience Engineering},
  school       = {Ghent University},
  title        = {Mathematical optimization methods for the analysis of compositional data: subset selection, unmixing and prediction},
  year         = {2014},
}