Artificial intelligence, Pattern recognition, Chromatography, Statistics and Analytical chemistry are her primary areas of study. Her research integrates issues of Machine learning, Multivariate statistics and Multivariate calibration in her study of Artificial intelligence. Anomaly detection is closely connected to Outlier in her research, which is encompassed under the umbrella topic of Multivariate statistics.
Her Chromatography research incorporates elements of Phase and Blind deconvolution. Desire Massart combines subjects such as Repeatability, Impurity and Near-infrared spectroscopy with her study of Analytical chemistry. Her Calibration research focuses on Chemometrics and how it connects with Algorithm.
Her primary areas of study are Chromatography, Artificial intelligence, Pattern recognition, Analytical chemistry and Statistics. Her Phase research extends to the thematically linked field of Chromatography. Her study in Artificial intelligence focuses on Pattern recognition, Principal component analysis, Feature selection, Linear discriminant analysis and Outlier.
Her Principal component analysis study frequently draws parallels with other fields, such as Chemometrics. Her Pattern recognition research includes elements of Cluster analysis, Set, Multivariate calibration and Data set. Calibration and Partial least squares regression are the subjects of her Statistics studies.
Desire Massart mainly investigates Chromatography, Statistics, Artificial intelligence, Pattern recognition and Algorithm. Her Chromatography study integrates concerns from other disciplines, such as Phase and Analytical chemistry. Her Partial least squares regression, Regression, Principal component regression and Total least squares study in the realm of Statistics interacts with subjects such as Variance.
The concepts of her Pattern recognition study are interwoven with issues in Boosting and Overfitting. Her Algorithm research is multidisciplinary, relying on both Calibration, Orthographic projection and Selection. Her studies examine the connections between Chemometrics and genetics, as well as such issues in Principal component analysis, with regards to Multiple factor analysis and Set.
Her primary areas of investigation include Chromatography, Chemometrics, Analytical chemistry, High-performance liquid chromatography and Phase. Her research on Chromatography focuses in particular on Reversed-phase chromatography. Her Chemometrics research is multidisciplinary, incorporating perspectives in Set, Regression, Environmental data, Environmental chemistry and Principal component analysis.
Her work in the fields of High-performance liquid chromatography, such as Monolithic HPLC column, intersects with other areas such as Variable elimination. Her work deals with themes such as Column and Selection, which intersect with Phase. Desire Massart has researched Iterative method in several fields, including Statistics, Artificial intelligence and Pattern recognition.
D. L. Massart;B. G. Vandeginste;L. M.C. Buydens;P. J. Lewi
Desiré L. Massart
V Centner;D L Massart;O E de Noord;S de Jong
Desire L. Massart;B. G. Vandeginste;L. M. Buydens;P. J. Lewi
Desiré L. Massart;Leonard Kaufman
Y Vander Heyden;A Nijhuis;J Smeyers-Verbeke;B.G.M Vandeginste
J. Luypaert;D.L. Massart;Y. Vander Heyden
P.F. de Aguiar;B. Bourguignon;M.S. Khots;D.L. Massart
B. Walczak;D.L. Massart
H.R. Keller;D.L. Massart
Yi Zeng. Liang;Olav M. Kvalheim;Hans R. Keller;D. Luc. Massart
C Hartmann;J Smeyers-Verbeke;D.L Massart;R.D McDowall
M. Daszykowski;Beata Walczak;Desire Massart
F. Cuesta Sanchez;J. Toft;B. Van Den Bogaert;D. L. Massart
M. Daszykowski;Beata Walczak;Desire Massart
E. Bouveresse;and C. Hartmann;D. L. Massart
Beata Walczak;Desire Massart
H.R. Keller;D.L. Massart
W. Wu;B. Walczak;D.L. Massart;S. Heuerding
W. Wu;Y. Mallet;B. Walczak;W. Penninckx
D. Coomans;D.L. Massart
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