What is he best known for?
The fields of study he is best known for:
- Statistics
- Organic chemistry
- Artificial intelligence
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.
His most cited work include:
- Principal component analysis (5888 citations)
- PLS-regression: a basic tool of chemometrics (5848 citations)
- Cross-Validatory Estimation of the Number of Components in Factor and Principal Components Models (2175 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Artificial intelligence (16.62%)
- Multivariate statistics (16.05%)
- Principal component analysis (14.33%)
What were the highlights of his more recent work (between 2001-2015)?
- Multivariate statistics (16.05%)
- Artificial intelligence (16.62%)
- Data mining (6.30%)
In recent papers he was focusing on the following fields of study:
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.
Between 2001 and 2015, his most popular works were:
- Orthogonal projections to latent structures (O-PLS) (1673 citations)
- CV‐ANOVA for significance testing of PLS and OPLS® models (474 citations)
- O2‐PLS, a two‐block (X–Y) latent variable regression (LVR) method with an integral OSC filter (257 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Organic chemistry
- Artificial intelligence
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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