What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Gene
Bart De Moor mostly deals with System identification, Genetics, Artificial intelligence, Data mining and Subspace topology.
His research in System identification intersects with topics in Kalman filter, Linear system, State space, Control theory and Matrix.
His State space study combines topics in areas such as Hankel matrix, Singular value decomposition and Mathematical optimization.
His research in Genetics focuses on subjects like Computational biology, which are connected to Candidate gene.
His Artificial intelligence research is multidisciplinary, incorporating elements of Machine learning and Pattern recognition.
His biological study spans a wide range of topics, including Basis, Linear subspace, Numerical linear algebra and Identification.
His most cited work include:
- A Multilinear Singular Value Decomposition (3096 citations)
- Least Squares Support Vector Machines (2730 citations)
- Subspace Identification for Linear Systems: Theory - Implementation - Applications (1824 citations)
What are the main themes of his work throughout his whole career to date?
Bart De Moor spends much of his time researching Artificial intelligence, System identification, Mathematical optimization, Algorithm and Control theory.
The various areas that Bart De Moor examines in his Artificial intelligence study include Machine learning, Data mining and Pattern recognition.
Bart De Moor has researched System identification in several fields, including Subspace topology, Dynamical systems theory and Numerical linear algebra.
As a member of one scientific family, he mostly works in the field of Numerical linear algebra, focusing on Singular value decomposition and, on occasion, Matrix.
His Mathematical optimization research integrates issues from Applied mathematics and Convex optimization.
His Support vector machine study focuses on Least squares support vector machine in particular.
He most often published in these fields:
- Artificial intelligence (15.67%)
- System identification (11.32%)
- Mathematical optimization (10.88%)
What were the highlights of his more recent work (between 2012-2021)?
- Artificial intelligence (15.67%)
- Data mining (9.47%)
- Machine learning (7.40%)
In recent papers he was focusing on the following fields of study:
Artificial intelligence, Data mining, Machine learning, Algorithm and Applied mathematics are his primary areas of study.
Bart De Moor interconnects Field, Binary number and Pattern recognition in the investigation of issues within Artificial intelligence.
His study in the field of Data pre-processing also crosses realms of Context.
His work carried out in the field of Algorithm brings together such families of science as Radial basis function kernel, Kernel, Cluster analysis, Mathematical optimization and Numerical analysis.
His Applied mathematics research includes themes of Monomial, Singular value decomposition, Condition number, Optimization problem and Eigenvalues and eigenvectors.
Bart De Moor is interested in Least squares support vector machine, which is a field of Support vector machine.
Between 2012 and 2021, his most popular works were:
- eXtasy: variant prioritization by genomic data fusion (123 citations)
- Biomarkers of endometriosis (110 citations)
- Hyperparameter Search in Machine Learning (71 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Gene
His primary areas of study are Artificial intelligence, Machine learning, Support vector machine, Data mining and Hyperparameter.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Python, Atlas, Ranging and Pattern recognition.
His Machine learning research focuses on Set and how it connects with Linear model, State estimator, Current and Kalman filter.
His research integrates issues of Ensemble learning, Taylor series, Database normalization, Memory footprint and Robustness in his study of Support vector machine.
His Data mining study incorporates themes from Workflow, Software quality, Clustering high-dimensional data and CURE data clustering algorithm.
His work deals with themes such as Feature, Field, Search problem, Hyperparameter optimization and Element, which intersect with Hyperparameter.
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