2022 - Research.com Engineering and Technology in Sweden Leader Award
Svante Wold mainly focuses on Partial least squares regression, Artificial intelligence, Principal component analysis, Multivariate statistics and Statistics. His research in Partial least squares regression intersects with topics in Latent variable, Linear regression, Regression, Biological system and Regression analysis. Svante Wold interconnects Machine learning and Pattern recognition in the investigation of issues within Artificial intelligence.
His Principal component analysis research is multidisciplinary, incorporating perspectives in Algorithm, Singular value decomposition, Stereochemistry and Chemical process. His work deals with themes such as Quantitative structure–activity relationship, Multivariate analysis, Data mining and Chemometrics, which intersect with Multivariate statistics. His studies in Statistics integrate themes in fields like Econometrics and Applied mathematics.
Artificial intelligence, Multivariate statistics, Principal component analysis, Partial least squares regression and Quantitative structure–activity relationship are his primary areas of study. His study in Artificial intelligence is interdisciplinary in nature, drawing from both Machine learning, Chemometrics and Pattern recognition. His Multivariate statistics study is concerned with the larger field of Statistics.
Svante Wold focuses mostly in the field of Principal component analysis, narrowing it down to matters related to Stereochemistry and, in some cases, Amino acid. His Partial least squares regression research includes themes of Latent variable, Linear regression, Regression, Regression analysis and Algorithm. Many of his studies on Quantitative structure–activity relationship apply to Biochemical engineering as well.
His primary areas of study are Multivariate statistics, Artificial intelligence, Data mining, Chemometrics and Statistics. His Multivariate statistics study incorporates themes from Multivariate analysis and Sample. The concepts of his Artificial intelligence study are interwoven with issues in Machine learning, Partial least squares regression, Biological data and Pattern recognition.
His Data mining research is multidisciplinary, relying on both Quantitative structure–activity relationship, Process analytical technology and Real-time computing. Svante Wold has researched Chemometrics in several fields, including Organic chemist, Principal component analysis and Selection. His work on Multivariate calibration and Variables as part of general Statistics study is frequently linked to OPLS, Value and Control theory, bridging the gap between disciplines.
His scientific interests lie mostly in Quantitative structure–activity relationship, Partial least squares regression, Chemometrics, Statistics and Data mining. His Partial least squares regression research incorporates themes from Pattern recognition, Applied mathematics and Artificial intelligence. Svante Wold has included themes like Principal component analysis, Projection and Calibration in his Chemometrics study.
The study incorporates disciplines such as Computational biology, Data set and Analytical chemistry in addition to Principal component analysis. The study incorporates disciplines such as Multivariate analysis, Multivariate statistics, Volume and Information and Computer Science in addition to Data mining. His Multivariate statistics research is multidisciplinary, incorporating elements of Biological system, Preprocessor and Orthographic projection.
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Principal component analysis
Svante Wold;Kim H Esbensen;Kim H Esbensen;Paul Geladi;Paul Geladi.
Chemometrics and Intelligent Laboratory Systems (1987)
PLS-regression: a basic tool of chemometrics
Svante Wold;Michael Sjöström;Lennart Eriksson.
Chemometrics and Intelligent Laboratory Systems (2001)
Cross-Validatory Estimation of the Number of Components in Factor and Principal Components Models
The Collinearity Problem in Linear Regression. The Partial Least Squares (PLS) Approach to Generalized Inverses
S. Wold;A. Ruhe;H. Wold;W. J. Dunn.
Siam Journal on Scientific and Statistical Computing (1984)
Orthogonal projections to latent structures (O-PLS)
Johan Trygg;Svante Wold.
Journal of Chemometrics (2002)
Pattern recognition by means of disjoint principal components models
Pattern Recognition (1976)
Orthogonal signal correction of near-infrared spectra
Svante Wold;Henrik Antti;Fredrik Lindgren;Jerker Öhman.
Chemometrics and Intelligent Laboratory Systems (1998)
The multivariate calibration problem in chemistry solved by the PLS method
S. Wold;H. Martens;H. Wold.
Multi‐way principal components‐and PLS‐analysis
Svante Wold;Paul Geladi;Kim Esbensen;Jerker Öhman.
Journal of Chemometrics (1987)
Multivariate Data Analysis in Chemistry
Svante Wold;C. Albano;W. J. Dunn;U. Edlund.
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