2022 - Research.com Engineering and Technology in Belgium Leader Award
2017 - SIAM Fellow For contributions to concepts and algorithms in numerical multilinear algebra and applications in engineering.
2004 - IEEE Fellow For contributions to algebraic and numerical methods for systems and control.
B. De Moor mostly deals with Support vector machine, Least squares support vector machine, Artificial intelligence, Mathematical optimization and Applied mathematics. The concepts of his Support vector machine study are interwoven with issues in Econometrics, Bayesian inference, Test data and Algorithm, Least squares. In his study, Optimization problem, Constrained optimization, State space, Kernel embedding of distributions and Principal component regression is inextricably linked to Radial basis function kernel, which falls within the broad field of Least squares support vector machine.
B. De Moor studied Artificial intelligence and Pattern recognition that intersect with Speech recognition. The Mathematical optimization study combines topics in areas such as Distributed generation, Singular value decomposition, Topology and Voltage. His research integrates issues of Electronic circuit, Linear system, Combinatorics, Matrix and Iterative method in his study of Applied mathematics.
His scientific interests lie mostly in Mathematical optimization, Control theory, Artificial intelligence, Algorithm and Applied mathematics. His Mathematical optimization study combines topics in areas such as Least squares and Convex optimization. His Control theory research integrates issues from Model predictive control and Identification.
B. De Moor has researched Artificial intelligence in several fields, including Machine learning and Pattern recognition. B. De Moor works mostly in the field of Algorithm, limiting it down to topics relating to Numerical linear algebra and, in certain cases, System identification, as a part of the same area of interest. His Applied mathematics study integrates concerns from other disciplines, such as Matrix and Linear system.
His primary areas of study are Obstetrics, Ultrasound, Gynecology, Surgery and Radiology. His biological study spans a wide range of topics, including Miscarriage, Pregnancy, First trimester, Gestation and Percentile. His Ultrasound research includes elements of Visualization, Medical physics and Accuracy and precision.
His Gynecology research is multidisciplinary, incorporating perspectives in Logistic regression, Luteal phase, Transvaginal ultrasound, Endometrial cancer and Crown-rump length. B. De Moor combines subjects such as Anesthesia and Abdominal circumference with his study of Surgery. The various areas that B. De Moor examines in his Radiology study include Stage, Endometrial Tumor, Risk of malignancy and Pattern recognition.
His primary scientific interests are in Gynecology, Ultrasound, Least squares support vector machine, Endometriosis and Luteal phase. His Gynecology research incorporates themes from Fetal medicine, Logistic regression, Gestational age and Crown-rump length. His Ultrasound research incorporates elements of Risk of malignancy, Prospective cohort study, Surgery and Nuclear medicine.
B. De Moor has included themes like Mathematical optimization and Cluster analysis in his Least squares support vector machine study. B. De Moor interconnects Least squares and Pattern recognition in the investigation of issues within Artificial intelligence. His Support vector machine research is multidisciplinary, incorporating elements of Computational complexity theory, Algorithm design and k-means clustering.
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Four qubits can be entangled in nine different ways
Frank Verstraete;Frank Verstraete;J Dehaene;B De Moor;Henri Verschelde.
Physical Review A (2002)
Financial time series prediction using least squares support vector machines within the evidence framework
T. Van Gestel;J.A.K. Suykens;D.-E. Baestaens;A. Lambrechts.
IEEE Transactions on Neural Networks (2001)
Optimal control by least squares support vector machines
J. A. K. Suykens;J. Vandewalle;B. De Moor.
Neural Networks (2001)
Artificial Neural Networks for Modelling and Control of Non-Linear Systems
Johan A. K. Suykens;Joos P. L. Vandewalle;B. L. de Moor.
Fetal electrocardiogram extraction by blind source subspace separation
L. de Lathauwer;B. de Moor;J. Vandewalle.
IEEE Transactions on Biomedical Engineering (2000)
Emerging patterns of cryptic chromosomal imbalance in patients with idiopathic mental retardation and multiple congenital anomalies: a new series of 140 patients and review of published reports
B. Menten;N. Maas;B. Thienpont;K. Buysse.
Journal of Medical Genetics (2006)
Bayesian framework for least-squares support vector machine classifiers, Gaussian processes, and kernel fisher discriminant analysis
T. Van Gestel;J. A. K. Suykens;G. Lanckriet;A. Lambrechts.
Neural Computation (2002)
Short-term load forecasting, profile identification, and customer segmentation: a methodology based on periodic time series
M. Espinoza;C. Joye;R. Belmans;B. De Moor.
IEEE Transactions on Power Systems (2005)
HE4 and CA125 as a diagnostic test in ovarian cancer: prospective validation of the Risk of Ovarian Malignancy Algorithm
T Van Gorp;I Cadron;E Despierre;A Daemen.
British Journal of Cancer (2011)
Terms, definitions and measurements to describe sonographic features of myometrium and uterine masses: a consensus opinion from the Morphological Uterus Sonographic Assessment (MUSA) group
T. van den Bosch;M. Dueholm;F. P. G. Leone;Lil Valentin.
Ultrasound in Obstetrics & Gynecology (2015)
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